Expertise Depth Analyzer — Evaluating Content Authority Through Multi-Signal Semantic Indicators

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The Expertise Depth Analyzer is a comprehensive evaluation system designed to measure how effectively a webpage demonstrates authoritative, expert-level content. It processes one or multiple URLs, breaks each page into meaningful content sections, and assesses these sections across five core semantic expertise signals—depth, terminology accuracy, reasoning structure, contextual richness, and confidence clarity. By analyzing these signals collectively, the system produces a holistic expertise profile for every section and for the page as a whole.

The evaluation workflow combines structured text extraction, refined preprocessing, and consistent chunk segmentation to ensure each section represents a focused semantic unit. These sections are then analyzed using specialized algorithms that quantify expert characteristics such as conceptual depth, precision of domain terminology, presence of reasoning patterns, contextual grounding, and assertive clarity. All scores are normalized and aggregated into a unified expertise score for each section, enabling fine-grained comparisons within and across pages.

Beyond individual section scoring, the system generates page-level scorecards, highlights strongest and weakest areas, and identifies systemic expertise gaps. The analyzer categorizes missing signals, low-performing components, and high-variance patterns, making it straightforward to pinpoint content weaknesses and prioritize improvements. Every page is summarized with an authority gap report that indicates where expertise signals fall short, where variance suggests inconsistency, and where targeted interventions can elevate content authority.

This comprehensive evaluation—spanning section-level insights, multi-component scoring, authority gap diagnostics, and actionable recommendations—allows SEO strategists and decision-makers to understand the true expertise depth of their content. The analyzer translates raw content into structured, interpretable, and improvement-focused expertise intelligence.

Project Purpose

The Expertise Depth Analyzer is designed to provide a structured and measurable understanding of how strongly a webpage communicates expertise and authority. Modern search evaluation increasingly depends on signals that demonstrate depth, precision, clarity, and contextual grounding rather than surface-level keyword matching. This system addresses that requirement by converting complex content characteristics into interpretable semantic indicators that can guide strategic enhancements.

The purpose of the project is to deliver a dependable, repeatable method for evaluating expertise-related signals across any webpage, regardless of topic or format. By breaking content into focused sections and applying multi-signal semantic measurements, the analyzer reveals where a page communicates expert-level understanding and where it lacks depth or clarity. This enables stakeholders to diagnose weaknesses that are often invisible during manual review, such as shallow conceptual coverage, limited reasoning structure, inconsistent terminology use, or insufficient contextual framing.

A central goal of the project is to support content improvement decisions. The analyzer not only scores each expertise component but also consolidates results into gap profiles, systemic low-signal indicators, and prioritized recommendations for strengthening content authority. The purpose extends beyond measurement—it equips content teams with precise, actionable insights that guide enhancements in depth, reasoning, terminology precision, and contextual support.

Ultimately, this project aims to make the evaluation of content expertise more transparent, more structured, and more aligned with real-world indicators of authority. By offering clear diagnostics and improvement paths, it helps elevate content quality in a way that supports credibility, improves user trust, and aligns with modern search quality expectations.

Project’s Key Topics Explanation and Understanding

Understanding Expertise Depth in SEO Content

Expertise depth represents the degree to which a piece of content demonstrates true subject-matter authority. In modern search environments, content cannot rely solely on keyword presence or superficial topic coverage; it must display a clear command of the subject through accurate terminology, contextual awareness, and well-reasoned explanations. Expertise depth is therefore a semantic property of the text itself. It reflects how comprehensively a topic is addressed, how precisely concepts are connected, and how confidently the content communicates knowledge. This project focuses on identifying these qualities and translating them into measurable indicators that reveal the true informational strength of a page.

Semantic Authority Signals and Their Role in Content Evaluation

Semantic authority signals are linguistic and conceptual patterns that naturally emerge in expert-level writing. These signals include the correct use of domain-specific terminology, coherent conceptual structures, consistent reasoning patterns, and a communication style associated with experienced practitioners. They show that the content has been created with an understanding that goes beyond surface-level SEO optimization. By analysing these signals within each content section, the system can estimate the degree of expertise exhibited, determine whether the content aligns with authoritative writing standards, and identify where depth or precision is lacking. This provides a stronger foundation for evaluating content quality than relying on heuristics or manual checks.

Multi-Signal Semantic Indicators for Authority Measurement

Expertise is multifaceted, and no single feature can capture it fully. For that reason, this project introduces a multi-signal evaluation framework that assesses content through a combination of complementary semantic dimensions. Each dimension reflects a different aspect of authoritative writing:

1. Depth

The depth indicator evaluates how thoroughly a concept is explored within the text. Expert content does not merely touch on a topic; it expands it, connects supporting details, addresses relevant subtopics, and develops explanations that help readers understand the underlying principles. This indicator captures how complete and substantive the content is relative to expectations for a knowledgeable source.

2. Terminology

Terminology use is one of the strongest semantic markers of expertise. When writers demonstrate fluency in domain-specific language and consistently apply technical terms correctly, it signals subject-matter understanding. This indicator measures the density, accuracy, and contextual correctness of domain terminology throughout the content.

3. Reasoning

Expert content is structured on clear reasoning patterns. It presents information logically, avoids contradictions, explains relationships between concepts, and often provides rationale or justification for claims. This indicator reflects the presence and clarity of such reasoning behaviour, helping determine whether the content communicates like an informed professional.

4. Contextual Awareness

Contextually aware writing demonstrates understanding of how individual points fit into the broader domain. It connects ideas, acknowledges relevant variations or limitations, and situates information within a larger thematic or practical framework. This indicator evaluates whether the content shows meaningful awareness of related concepts rather than addressing topics in isolation.

5. Confidence

Confidence in expert writing emerges from clarity, directness, and the absence of unnecessary hedging. It represents the writer’s ability to articulate information with precision and certainty while avoiding ambiguity. This indicator does not measure tone for marketing purposes; it measures the implicit confidence associated with expert-level explanation.

Together, these five indicators form a comprehensive semantic profile of content authority. Each one contributes uniquely, and the combined evaluation allows the system to capture nuanced expertise signals that are not visible through keyword analysis or structural heuristics.

Why Section-Level Analysis Matters

Evaluating expertise at the section level enables a far more accurate and precise understanding of content quality. Entire pages often contain sections of varying strength—some may exhibit strong expertise, while others may be superficial or lack clarity. By scoring each section independently, the system can identify strong authoritative components, locate weak areas, diagnose inconsistencies in depth, and reveal structural imbalances. This approach supports targeted improvements, helping elevate the entire page’s authority rather than treating it as a single undifferentiated unit.

Interpreting Expertise Through a Standardized Scoring Framework

To make semantic expertise signals actionable, the project translates complex linguistic patterns into normalized numerical scores. These scores represent the relative strength of each expertise dimension and make it possible to compare sections, track improvements, and develop data-driven recommendations. The combination of multi-signal indicators and standardized scoring creates a consistent interpretation layer that can be applied across content types and domains. This enables objective evaluation and ensures that the system’s outputs remain understandable, actionable, and aligned with professional SEO workflows.

Q&A: Understanding the Project’s Value, Features, and Importance

How does this project help evaluate whether a page demonstrates true expertise?

This project provides a structured, evidence-driven way to assess whether a page communicates knowledge at an expert level. Instead of relying on surface checks such as keyword presence, word count, or manual heuristics, the system examines the semantic fabric of the text itself. It identifies linguistic patterns, conceptual structures, terminology usage, and reasoning behaviour that are naturally present in authoritative writing. Each section of the page is processed independently, allowing the system to detect where expertise is strong, where it is missing, and how evenly it is distributed across the content. This transforms a previously subjective and time-consuming judgment into a consistent and repeatable evaluation method. The resulting insights help ensure that content aligns with modern expectations for depth, clarity, and domain authority—fundamentally improving the competitiveness of SEO pages in environments where expertise signals matter more than ever.

Why is a multi-signal semantic approach more reliable than traditional content quality checks?

Traditional content evaluation methods often look at isolated factors such as keyword density, readability scores, or the presence of headings. While useful, these signals do not reflect whether the content is genuinely knowledgeable or authoritative. A multi-signal semantic approach analyses the internal meaning and behaviour of the writing. It looks at how ideas are introduced, expanded, connected, and justified. It measures correct terminology usage, conceptual depth, reasoning clarity, contextual awareness, and implicit confidence—qualities difficult to fake and impossible to capture with simple heuristics. Because these signals arise naturally in expert-level writing, evaluating them provides a far more accurate indication of true content quality. This leads to a more trustworthy assessment that aligns closely with how modern search systems interpret and reward expertise-driven content.

How does the system identify strengths and weaknesses in a page’s authority signals?

The system evaluates each section of the content using five semantic indicators—depth, terminology, reasoning, contextual awareness, and confidence. By scoring each indicator individually, the system uncovers specific areas where the writing excels or falls short. For example, a section might demonstrate strong terminology usage but weak reasoning patterns, indicating that the content uses technical language without expanding on underlying concepts. In another case, contextual awareness may be lacking, revealing that the section does not connect its ideas to related topics or broader implications. Because the system works at the section level, it creates a detailed map of strengths and weaknesses across the page. This allows for targeted improvements instead of broad, unfocused revisions. It also enables SEO teams to understand how expertise is distributed and whether certain high-value sections lack authority signals that are essential for ranking competitiveness.

How does section-level scoring improve the practical value of the analysis?

Section-level scoring ensures that insights are precise and actionable. Pages often contain a mix of strong and weak content. Without examining sections independently, weaknesses become hidden within the overall text, and strengths may not be recognized accurately. By evaluating each section separately, the analysis reveals the structural composition of the content: where depth is concentrated, where superficial writing appears, and how the narrative flow influences authority perception. This is particularly useful in SEO contexts where certain sections—such as introductions, explanations, or technical breakdowns—carry more weight in establishing expertise. Section-level scoring helps identify which pieces require rewriting, which ones are performing well, and how the page can be reorganized or expanded to achieve a more balanced and authoritative structure.

In what way does this project support E-E-A-T-oriented content improvement?

Modern search systems emphasise Expertise, Experience, Authoritativeness, and Trust. One of the most challenging aspects of E-E-A-T evaluation is determining whether the text truly reflects expert knowledge rather than generic information. This project directly supports E-E-A-T objectives by measuring semantic characteristics associated with knowledgeable writing. It identifies where terminology is used authentically, where conceptual depth is present, where reasoning is clear and defensible, and where the content reflects contextual understanding. These are the same qualities human evaluators look for when determining whether a page demonstrates authority within a domain. By quantifying these signals, the system allows SEO professionals to align content with E-E-A-T expectations in a methodical and measurable way, ultimately contributing to stronger authority positioning and improved search competitiveness.

What practical value does this system deliver for content enhancement and optimization workflows?

The system integrates naturally into real-world content improvement processes. It highlights sections that require deeper explanation, identifies terminology gaps, reveals reasoning inconsistencies, and pinpoints where contextual signals are missing. These insights allow teams to make revisions that directly strengthen the perceived expertise of the content. Instead of guessing what to rewrite or expand, teams receive targeted guidance based on semantic indicators that reflect true subject-matter understanding. This creates a more efficient workflow, reduces revision cycles, and ensures that improvements address high-impact areas rather than surface-level adjustments. The resulting content becomes more aligned with expert writing standards, more capable of answering user needs, and better positioned to compete in search environments that value depth and expertise.

Why is this project important for SEO teams working across diverse content domains?

Different industries and content types require different expressions of expertise. A technical guide, a medical article, a legal explanation, or an engineering breakdown all rely on terminology, conceptual structures, and reasoning patterns specific to their respective fields. This project adapts easily across domains because its semantic indicators are not tied to one specific topic or industry. Instead, they focus on linguistic behaviours that reliably occur in authoritative writing across fields. This makes the system highly versatile and suitable for evaluating a wide range of content types. SEO teams working on varied topics can uniformly assess expertise depth, compare performance across pages, and maintain consistent quality standards regardless of domain complexity.

How does this project help justify content improvements to stakeholders?

One of the challenges in SEO is explaining why certain content needs to be improved or restructured. This project provides clear, measurable, and intelligible insights that can be communicated to stakeholders without ambiguity. By presenting scores for depth, terminology, reasoning, contextual awareness, and confidence, along with visualizations and section-level breakdowns, the system transforms complex semantic behaviour into structured information. This allows teams to demonstrate why certain changes are necessary, why specific sections require rewriting, and how improvements will strengthen perceived expertise. The analysis becomes a transparent justification tool—supporting strategic decisions with objective evidence rather than opinion or intuition.

Libraries Used in the Project

time

The time module is a standard Python library that provides access to time-related functions such as timestamps, delays, and execution duration measurement. It is commonly used in performance-sensitive applications to measure processing time, schedule operations, or manage retries. Although lightweight, it plays a critical role in controlling temporal behaviour in program execution.

In this project, time supports functions that require waiting or reattempting operations, particularly during HTTP requests or model loading procedures. When fetching webpages or performing repetitive tasks, network-related operations can occasionally fail or respond slowly. Introducing controlled delays through this module helps stabilize workflows and ensures repeat operations function predictably. The inclusion of the time library contributes to overall robustness and reliability across different components of the pipeline.

re

The re module provides regular expression capabilities in Python, enabling powerful and flexible pattern matching. It allows extraction, cleaning, and restructuring of text through patterns rather than fixed string rules. This makes it invaluable for working with unstructured or semi-structured data, especially in text-heavy environments.

Within this project, re is heavily used for URL cleaning, label trimming, text normalization, and structural adjustments during preprocessing. Many steps—such as removing unwanted characters, identifying HTML patterns, truncating long strings, or detecting noise—depend on pattern-based rules. Regular expressions help maintain consistency and accuracy when transforming text across different web pages, ensuring that all downstream semantic operations work on clean, well-prepared data.

html (html_lib)

The Python html module offers utilities for escaping, unescaping, and managing HTML entities in text. It is especially useful when dealing with content extracted from web pages, where encoded characters or special symbols may appear in forms not suitable for direct text processing.

In this project, html_lib is used to normalize HTML-encoded characters within extracted text blocks, ensuring clean human-readable content. This avoids issues where special characters—such as quotes, symbols, or accented characters—remain encoded and disrupt tokenization or embedding generation. By resolving HTML entities early, the project maintains consistent text quality throughout the preprocessing and chunking pipeline.

hashlib

The hashlib library provides secure hashing algorithms such as MD5 and SHA variants. Hashing is essential when unique identification is needed without storing entire content pieces. A hash value stays stable as long as the underlying text remains unchanged.

Here, hashlib is used to generate stable, compact section identifiers based on the extracted text. Instead of storing long text strings in identifiers or using sequential IDs that may break after text changes, hashing ensures that each section has a unique, repeatable fingerprint. This contributes to reproducibility, easy reference, and reduced storage overhead during section-level scoring, clustering, and visualization steps.

unicodedata

The unicodedata module provides access to Unicode database properties, allowing normalization, category detection, and character-level transformations. This is especially important for web content, where characters may appear in visually similar but technically different encoded forms.

In this project, Unicode normalization is applied during text cleanup to ensure that characters with diacritics, accented letters, or compatibility variants are standardized. This prevents inconsistencies during tokenization, matching, or vector generation. Using this module ensures high-quality, uniform text that improves embedding reliability and similarity measurement accuracy.

gc

Python’s gc (garbage collector) library allows manual interaction with the memory management subsystem. It provides tools to free unused objects and optimize runtime memory usage—particularly valuable in heavy text and model workloads.

This project processes multiple large pages, generating embeddings and running clustering operations that can be memory-intensive. Calling gc.collect() strategically prevents memory leaks and ensures efficient resource usage, especially on systems with limited memory such as notebooks or shared server environments. It helps maintain stable performance throughout long-running sessions.

logging

The logging library is Python’s standard facility for tracking events during execution. It supports different severity levels (info, debug, warning, error) and allows structured, timestamped messages.

In this project, logging is used to monitor key operations such as model loading, extraction flow, preprocessing decisions, and scoring behaviour. Detailed logs provide a transparent view of system behaviour, assisting in debugging and ensuring operational clarity. This is especially important in real-world client-oriented settings, where predictable and traceable execution is essential for maintenance and reliability.

requests

The requests library is a widely used tool for sending HTTP requests. Known for its simplicity and robustness, it allows easy communication with web servers and handles responses, errors, and redirects gracefully.

Here, requests enables downloading webpage HTML for analysis. Since the entire project depends on extracting text from client-provided URLs, reliable and configurable HTTP handling is critical. Built-in timeout, retry, and error-handling capabilities of requests ensure stable fetching across different domains, server behaviours, and network conditions.

BeautifulSoup (bs4)

BeautifulSoup is a flexible library for parsing and navigating HTML documents. It simplifies tasks such as finding elements, removing unwanted tags, and restructuring content in a tree-based format.

This project uses BeautifulSoup extensively to clean and extract meaningful textual sections from website HTML. It removes noise elements such as navigation bars, scripts, footers, ads, and comments, isolating only the meaningful body content. Clean structural extraction is a fundamental part of producing reliable semantic sections for downstream scoring.

math

The math module provides mathematical tools including rounding, distance computation, statistical transforms, and trigonometric functions. Even though Python offers built-in arithmetic, math is preferred for accuracy and performance.

In this project, math assists in various calculations such as distance thresholds, score adjustments, and normalization factors used in semantic scoring or vector-based clustering decisions. It ensures consistent numerical behavior across all quantitative operations.

numpy (np)

NumPy is the foundational scientific computing library in Python, offering fast multi-dimensional arrays and vectorized operations. Its performance advantages make it essential for numerical and embedding-oriented workloads.

Here, NumPy powers the internal operations of scoring, embeddings manipulation, normalization calculations, and clustering preparation. Large collections of vector-based values—such as section embeddings or component scores—benefit greatly from NumPy’s optimized routines, ensuring speed and reliability.

pandas (pd)

Pandas is the industry-standard library for working with structured data, offering intuitive dataframes and a rich API for filtering, sorting, grouping, and aggregating.

In this project, pandas is used to convert processed results into structured formats that support visualization, inspection, and report generation. Section-level scoring, component comparisons, and page summaries are all organized as dataframes, making downstream analysis clear and consistent.

AgglomerativeClustering

Agglomerative clustering is part of scikit-learn’s unsupervised learning toolkit. It builds hierarchical clusters by progressively merging similar items, creating structures that reveal natural groupings within data.

In this project, clustering is used during the section grouping phase to combine semantically similar chunks. This ensures that conceptually related portions of the content are identified and treated together, stabilizing expertise scoring and improving the interpretability of section-level insights.

nltk + sent_tokenize

NLTK (Natural Language Toolkit) is a long-standing library for linguistic processing, providing tokenizers, stemmers, corpora, and sentence boundary detection.

This project uses NLTK’s sentence tokenizer to split extracted sections into clean, well-defined sentences. This ensures that semantic models receive coherent text units during embedding, improving accuracy and reducing noise. Sentence-level segmentation is essential for high-quality chunking and stable semantic interpretations.

SentenceTransformer + cos_sim + dot_score

SentenceTransformers is a framework built on top of transformers for generating sentence-level vector embeddings. It provides pre-trained models optimized for semantic similarity, clustering, and retrieval tasks.

In this project, embeddings generated by SentenceTransformers form the basis for measuring semantic features like depth, terminology behavior, and contextual coherence. Cosine similarity (cos_sim) and dot-product scoring (dot_score) measure semantic alignment and component correlations. These embeddings underpin all multi-signal scoring operations in the expertise evaluation pipeline.

torch

PyTorch is a widely adopted deep learning framework providing tensor operations, GPU acceleration, and model execution capabilities.

PyTorch is used indirectly to manage the embedding model’s device placement, ensuring that model inference runs efficiently on either CPU or GPU. It also provides core tensor operations required by SentenceTransformers to generate embeddings.

transformers + pipeline

The transformers library from Hugging Face offers state-of-the-art pre-trained models and high-level APIs such as pipelines for easy inference.

In this project, the pipeline utility is used in a limited but important context: to support auxiliary scoring behaviours or fallback models if needed. It ensures operational flexibility and gives the system adaptability when different semantic models are required for specific tasks or diagnostic checks.

matplotlib.pyplot

Matplotlib is Python’s foundational plotting library, enabling detailed and customizable visualizations in both exploratory analysis and reporting.

This project uses Matplotlib to generate all visual outputs, including bar charts, heatmaps, distribution plots, and comparative graphs. These visualizations help make expertise scoring understandable, interpretable, and client-friendly, allowing non-technical users to grasp insights immediately.

seaborn

Seaborn builds on Matplotlib with polished, statistically oriented visual styles and high-level plotting functions. It produces clean, modern graphics well-suited for client reporting.

In this project, Seaborn provides the visual foundation for expertise score charts, component comparisons, and heatmaps. Its aesthetic defaults enhance readability and ensure a professional appearance in all visual outputs. Combined with Matplotlib, it delivers refined and clear visual representations of complex semantic behaviour.

Function: fetch_html

Overview

The fetch_html function is responsible for retrieving the raw HTML content of a webpage from a given URL. It acts as the foundational entry point of the entire analysis pipeline, as every subsequent step—cleaning, parsing, chunking, embedding, and semantic scoring—depends on reliable and complete HTML extraction. To ensure stability in real-world environments where server responses may vary widely, this function incorporates retry mechanisms, exponential backoff, configurable timeouts, and flexible user-agent handling.

The function accounts for common issues observed during large-scale web extraction, such as intermittent server failures, slow responses, temporary network disruptions, and inconsistent encoding. By applying multiple encoding heuristics, it maximizes the chances of producing readable and structurally usable HTML. If all attempts fail, the function raises a clear runtime error rather than returning incomplete or corrupted content. This ensures deterministic behavior and protects the downstream semantic processing pipeline from ingesting unreliable input.

The combination of stability provisions, fault tolerance, and encoding robustness makes this function production-ready. It allows the system to operate consistently across a broad range of websites—blogs, documentation pages, service pages, and complex marketing sites—without requiring manual intervention.

Key Code Explanations

1. Retry Loop with Exponential Backoff

while attempt <= max_retries:

and

wait = backoff_factor ** attempt

time.sleep(wait)

This loop ensures that the function keeps attempting the request even if the server fails to respond the first time. The exponential backoff mechanism increases the wait time after each failed attempt, helping to avoid aggressive repeated requests that could trigger rate limits or worsen server overload. This is a critical component for real-world production reliability, especially when dealing with diverse external websites where server behavior cannot be controlled.

2. Core HTTP Request with Safety Controls

resp = requests.get(

    url,

    headers=headers,

    timeout=request_timeout,

    allow_redirects=True

)

resp.raise_for_status()

The HTTP request includes three key safeguards:

• Custom headers mimic a real browser user agent, significantly increasing the chance of receiving full content instead of bot-blocked versions.
• Timeout ensures that long-hanging requests do not freeze the pipeline.
• raise_for_status() immediately flags HTTP-level failures such as 403, 404, or 500 errors, enabling the retry logic to take over. Combined, these steps enforce controlled and predictable network behavior across all URL inputs.

3. Multi-Strategy Encoding Detection

enc_choices = [resp.apparent_encoding, “utf-8”, “iso-8859-1”]

and

resp.encoding = enc

html = resp.text

if html and len(html.strip()) > 80:

    return html

Webpages vary widely in encoding formats. Relying on a single encoding may produce unreadable text or corrupt HTML structure. This block tries multiple encodings—including the server-suggested one and widely used alternatives—to extract a usable HTML body. The additional length check ensures that trivially short or invalid bodies are not accepted. This preventive measure guards downstream operations from receiving incomplete or malformed content.

Function _safe_normalize

Overview

The _safe_normalize function performs foundational text cleaning to ensure that all extracted content is readable, structurally consistent, and suitable for downstream semantic processing. Since the project evaluates expertise depth and content authority, the quality of the text fed into the semantic models directly influences the accuracy of expertise-signal detection. This function therefore removes noise and inconsistencies at the character and whitespace level, ensuring the content is uniformly normalized before embedding, clustering, scoring, or any expertise-depth analysis.

It handles HTML-escaped characters, inconsistent unicode representations, and irregular whitespace—common issues in real-world webpages. By converting all incoming text into a standard normalized format, the function guarantees that semantic similarity calculations and embedding generation behave predictably across different pages and websites. As this function is small and primarily self-explanatory, the detailed code explanation subsection is optional, yet the overview already captures its role in the overall workflow.

Key Code Explanations

txt = html_lib.unescape(text)

This step converts encoded HTML entities (e.g., &amp;, &lt;) into their readable characters. Expertise-signal extraction requires that text reflect the real wording used on the page, and unescaping ensures that the content is semantically accurate before further processing.

txt = unicodedata.normalize(“NFKC”, txt)

Unicode normalization is critical because multiple unicode forms can visually appear identical while being technically different. Normalizing into NFKC form ensures consistent handling of symbols, alphanumeric characters, and punctuation across pages, preventing mismatches during embedding generation.

txt = re.sub(r”[\r\n\t]+”, ” “, txt)

This removes formatting characters that typically serve structural purposes in HTML but create noise in sentence-level processing. Replacing them with spaces preserves word boundaries without harming semantic structure.

txt = re.sub(r”\s+”, ” “, txt).strip()

This collapses excessive spaces into a clean, single-space structure, ensuring the text remains compact and readable. Clean whitespace is essential for accurate sentence tokenization and for eliminating meaningless spacing variations that could affect semantic scoring.

Function clean_html

Overview

The clean_html function performs structural sanitization of the webpage by removing unnecessary, irrelevant, or noisy HTML components. For a project focused on evaluating content authority and semantic expertise signals, isolating the main body of meaningful text is a foundational requirement. Raw webpages typically contain scripts, styles, navigation elements, multimedia, and advertising components that do not contribute to topical expertise. These elements can distort embedding distributions and reduce semantic clarity if left in place.

The function parses the incoming HTML into a BeautifulSoup object and then strips away all tags and comment elements that are not relevant to content expertise. By doing so, it ensures the extracted content reflects only the substantive informational layers of the page. This is essential for analyzing how deeply a page explores its topic, whether it provides authoritative explanations, and how consistently it maintains expert-level language. The cleaned output from this function is passed into the next stages of segmentation, embedding, scoring, and expertise-signal evaluation.

Key Code Explanations

soup = BeautifulSoup(html_content, “lxml”)

The function first attempts to parse the HTML using the lxml parser, which is faster and more robust for complex webpage structures. Clean parsing is essential because inaccurate DOM structures lead to unreliable text extraction and weaken semantic scoring. If parsing fails, it gracefully falls back to the default html.parser, ensuring resilience across a wide range of page formats.

remove_tags = [“script”, “style”, “noscript”, “iframe”, “svg”, “canvas”, …]

These tags represent non-content components. Removing them ensures that only the textual, topic-relevant parts of the page remain. Since this project evaluates expertise signals, removing decorative or functional elements prevents noise from contaminating section-level semantic analysis.

for c in soup.find_all(string=lambda x: isinstance(x, Comment)):

    c.extract()

HTML comments sometimes contain debugging notes, hidden scripts, or metadata irrelevant to the main content. Removing them guarantees that the analysis is focused purely on user-visible, meaningful text—the area where expertise is communicated.

Function _md5_hex

Overview

The _md5_hex function generates a unique identifier for any given text by applying the MD5 hashing algorithm and returning the hexadecimal digest. Within this project, every content section needs a consistent and unique ID to track it across processing pipelines, store signals, attach scores, and reference sections in visualizations or reports. Using an MD5 hash ensures that even sections with identical headings or similar content can be uniquely identified based on their text and position.

This approach simplifies data handling, especially when multiple sections might share headings like “Introduction” or “Overview”. It avoids collisions and allows reliable indexing of section-level analyses such as semantic depth, terminology, reasoning, and contextual richness.

Key Code Explanations

return hashlib.md5(text.encode(“utf-8”)).hexdigest()

The input text is first encoded in UTF-8 to handle all possible characters. The MD5 hashing function then converts this byte representation into a fixed-length hexadecimal string. This string serves as the section’s unique identifier. MD5 is chosen for its efficiency and deterministic output, which is adequate for generating non-security-critical IDs in this project.

Function extract_sections

Overview

The extract_sections function transforms the cleaned HTML content into logical content blocks, which are the fundamental units for expertise analysis. Each section represents a coherent part of the page where semantic expertise signals can be measured. The function follows a two-tier strategy:

1.    Heading-based extraction: If the page contains structured headings (h2, h3, h4), the content under each heading is grouped as a separate section. Headings naturally represent topical boundaries and allow for precise mapping of signals like reasoning patterns, domain terminology, and contextual richness.

2.    Paragraph fallback extraction: If the page lacks heading structure, the function groups paragraphs (p tags) or list items (li tags) into blocks of roughly fallback_para_words words. This ensures that even unstructured pages can be analyzed in meaningful semantic chunks.

Each section includes a section ID, heading, raw text, and position, ensuring consistent downstream processing. Sections shorter than min_section_chars are ignored to filter out irrelevant noise or extremely short content that cannot provide meaningful expertise signals.

Key Code Explanations

headings = body.find_all(heading_tags)

The function searches for the specified heading tags in the body of the HTML. Headings are used as natural boundaries for sections, reflecting how humans organize content hierarchically. This step is critical for creating semantically meaningful sections.

if current and len(current[“raw_text”].strip()) >= min_section_chars:

    src = f”{current[‘heading’]}_{current[‘position’]}”

    current[“section_id”] = _md5_hex(src)

    sections.append(current)

Before starting a new section, the function finalizes the previous section if it contains enough content. The section ID is generated by combining the heading and position and hashing it with _md5_hex. This ensures each section is uniquely identifiable and preserves a mapping between its heading, text, and position in the page.

buffer.append(txt)

buffer_words.extend(w)

if len(buffer_words) >= fallback_para_words:

    …

When no headings are present, paragraphs are accumulated into a buffer until a target word count is reached. This buffer becomes a fallback section, ensuring sufficient textual content is grouped together for semantic evaluation. This mechanism guarantees consistent sectioning across unstructured pages.

raw = ” “.join(buffer)

sections.append({

    “section_id”: _md5_hex(sec_id_src),

    “heading”: f”Section {position}”,

    “raw_text”: raw,

    “position”: position

})

Once the buffer reaches the required word count or at the end of the content, the function finalizes the fallback section. The section receives a generated heading (“Section X”) and a unique MD5-based ID. This ensures all sections, whether heading-based or fallback, are standardized for subsequent expertise scoring.

Function _is_boilerplate

Overview

The _is_boilerplate function is a conservative detector for boilerplate content, identifying text segments that are unlikely to contribute meaningful semantic expertise signals. Boilerplate sections, such as privacy policies, cookie notices, copyright statements, sitemaps, and very short repetitive phrases, are typically present on almost every webpage and do not reflect the depth, reasoning, or contextual richness of the content. Removing these ensures that only valuable content is analyzed, preventing noise in the expertise scoring.

The function checks for the presence of common boilerplate phrases and considers the text length, dropping segments that are short and contain typical boilerplate tokens. It also allows for custom boilerplate terms to be added based on client-specific or domain-specific pages.

Key Code Explanations

bps = set(_REDUCED_BOILERPLATE + (boilerplate_terms or []))

for bp in bps:

    if bp in lower and len(lower.split()) < max_words_for_drop:

        return True

All default and custom boilerplate phrases are combined into a set. The function then iterates through these phrases and flags a section as boilerplate if the phrase exists in the text and the word count is below the threshold (max_words_for_drop). This ensures that only short, likely non-informative boilerplate sections are filtered out.

if len(lower.split()) < 6 and len(lower) < 120:

    return True

Very short and repetitive phrases are automatically classified as boilerplate. This heuristic prevents extremely small or fragmentary text blocks from being treated as valid content sections.

Function preprocess_section_text

Overview

The preprocess_section_text function normalizes and filters raw section text to ensure that only semantically meaningful and analyzable content is retained. Preprocessing is a critical step before scoring sections for expertise signals. Key operations include:

1. Text normalization: HTML unescaping, Unicode normalization, and whitespace collapsing via _safe_normalize.
2. Optional removal of URLs and inline numeric references: Ensures that extraneous information like links and reference numbers do not distort semantic or reasoning analyses.
3. Boilerplate filtering: Removes sections flagged by _is_boilerplate to focus on genuine content.
4. Short text filtering: Drops sections with very few words, which are unlikely to provide meaningful insights.
5. Code preservation: Optionally retains code snippets to allow analysis of technical content without being removed.

This function ensures that the section text is clean, relevant, and standardized for downstream scoring of depth, terminology, reasoning, contextual richness, and confidence indicators.

Key Code Explanations

text = _safe_normalize(raw_text)

The raw section text is normalized using _safe_normalize, which unescapes HTML entities, normalizes Unicode characters, and collapses multiple spaces and line breaks. This step standardizes text formatting and prepares it for further processing.

if remove_urls:

    text = re.sub(r”https?://\S+|www\.\S+”, ” “, text)

URLs are removed using a regular expression to prevent links from interfering with semantic analysis. Removing URLs ensures that scoring focuses on meaningful content rather than extraneous metadata.

if _is_boilerplate(text, boilerplate_terms=boilerplate_terms):

    return “”

Boilerplate sections, such as legal disclaimers or repeated short fragments, are discarded by checking against _is_boilerplate. This guarantees that the scoring system only evaluates content that reflects expertise.

if len(text.split()) < min_word_count:

    return “”

Very short sections are ignored, as they are unlikely to provide meaningful semantic signals. This minimum word threshold ensures that every analyzed section has sufficient content for reliable expertise scoring.

Function estimate_token_count

Overview

The estimate_token_count function provides a lightweight, approximate method for determining the token size of a text block. Instead of relying on computationally expensive tokenizer models, it simply counts whitespace-separated units. Although this approximation does not replicate model-specific tokenization with perfect accuracy, it offers a reliable fallback that is efficient, fast, and entirely sufficient for chunking decisions within this project. Since the project targets real-world SEO content, token estimation must remain performant and scalable even for large web pages or multi-URL workflows. This simple estimator fulfills that requirement without degrading the quality of downstream chunking or sectioning.

Function sliding_window_fallback

Overview

The sliding_window_fallback function generates text chunks using a sliding window mechanism applied directly to tokenized text. This method ensures that even very long and complex content can be segmented into manageable blocks when sentence-based chunking is not available or when individual sentences exceed length limits. The algorithm moves across the token list with a defined window size and overlap ratio, creating coherent overlapping chunks that protect against semantic fragmentation. This fallback mechanism is critical for robustness, preventing failures when encountering unusually structured content, long-form technical writing, or artificially elongated sentences common in autogenerated pages.

Key Code Explanations

tokens = text.split()

if len(tokens) <= window:

    return [” “.join(tokens)]

The text is split into tokens, and if the total is within the window limit, the function simply returns the full text as a single chunk. This avoids unnecessary processing for short texts.

start = 0

while start < len(tokens):

    end = start + window

    chunk_tokens = tokens[start:end]

A traditional sliding window approach is implemented. The algorithm takes window tokens at a time, ensuring that each chunk remains within the token capacity used throughout the project for section-level scoring.

start = max(end – overlap, start + 1)

This ensures overlapping chunks are created, maintaining semantic continuity between adjacent chunks. The fallback avoids losing important information positioned near chunk boundaries.

Function hybrid_chunk_section

Overview

The hybrid_chunk_section function is one of the core preprocessing utilities in the project. It produces semantically coherent, size-controlled text chunks by combining sentence-aware segmentation with token-based chunk size management. The method first attempts sentence tokenization to maintain natural linguistic boundaries, which is crucial for evaluating reasoning quality, terminology flow, contextual richness, and expertise signals. Sentences are progressively grouped into chunks until the token estimate reaches a structure-defining threshold. When encountering exceptionally long sentences that exceed the limit, the function gracefully falls back to the sliding-window method for that specific sentence.

This hybrid design provides the best of both worlds: semantic integrity through sentence grouping and reliability across all content types through the sliding window fallback. The final chunks are filtered to remove excessively short text blocks, ensuring that every chunk is substantial enough for meaningful semantic scoring.

Key Code Explanations

sentences = sent_tokenize(text)

if not sentences:

    return sliding_window_fallback(text, window=max_tokens, overlap=sliding_overlap)

If sentence tokenization fails (often due to insufficient punctuation or irregular formatting), the function automatically switches to a robust fallback method. This ensures uninterrupted processing across diverse page types.

current_chunk.append(sent)

token_est = estimate_token_count(” “.join(current_chunk))

if token_est > max_tokens:

Sentences are accumulated until the estimated token count exceeds the defined threshold. This adaptive accumulation ensures that chunks remain balanced—large enough to retain meaningful structure but small enough for efficient semantic processing.

if len(current_chunk) == 1:

    chunks.extend(sliding_window_fallback(current_chunk[0], window=max_tokens, overlap=sliding_overlap))

When a single sentence is longer than allowed, the function breaks it using the sliding window method. This avoids discarding long sentences that may contain valuable expertise signals or tightly packed technical details.

cleaned = [c.strip() for c in chunks if estimate_token_count(c.strip()) >= min_tokens]

Final filtering removes chunks that do not meet the minimum token threshold. This ensures that the scoring stage only processes text blocks with sufficient depth to evaluate expertise indicators reliably.

Function: extract_preprocess_and_chunk_page

Overview

This function serves as the end-to-end pipeline for transforming a raw webpage into a structured set of semantically usable text chunks. It integrates every major stage of the processing workflow—fetching HTML, cleaning the document, extracting meaningful sections, preprocessing text, and finally chunking content into model-ready units.

In the context of ranking model interpretability and semantic analysis, this function ensures that only clean, relevant, and token-efficient text enters the downstream NLP steps. It also standardizes the structure of processed pages by returning a consistent dictionary containing the page title, extracted sections, chunk IDs, estimated token counts, and any processing notes.

This function also contains safeguards at every stage, ensuring resilience against failures (e.g., fetch issues, parsing errors, missing titles, or invalid sections). When failures occur, the function returns structured diagnostic notes rather than breaking the pipeline.

Overall, this is the core foundation that transforms raw URLs into structured inputs that the rest of the analysis system can rely on.

Key Code Explanations

1. Fetching the HTML Content

html = fetch_html(url, request_timeout=request_timeout, delay=fetch_delay, max_retries=max_retries, backoff_factor=backoff_factor)

This line retrieves the raw HTML from the given URL using retry logic and configurable delays. It ensures that transient network issues do not break the pipeline by allowing multiple attempts with a backoff strategy. If this step fails, the function exits early with a “fetch_failed” note.

2. Cleaning and Normalizing HTML

soup = clean_html(html)

This converts the fetched HTML into a cleaned and readability-optimized BeautifulSoup object. It removes boilerplate elements (e.g., scripts, styles, navigation clutter) so that only meaningful textual content remains for section extraction.

3. Robust Page Title Extraction

title_tag = soup.find(“title”)

…

h1 = soup.find(“h1”)

title = _safe_normalize(h1.get_text()) if …

The function first attempts to extract the title from the <title> tag. If it is missing or unusable, it falls back to the first <h1> heading. If both fail, the function assigns “Untitled Page.” This ensures every processed page has a readable, user-friendly title—important for client reporting.

4. Extracting Structured Sections

raw_sections = extract_sections(

    soup,

    min_section_chars=min_section_chars,

    fallback_para_words=fallback_para_words

)

The function isolates the document into hierarchical or content-driven sections (headings/paragraph groups). It applies minimum character thresholds and fallback logic to ensure small or noisy fragments are not treated as sections.

5. Preprocessing Text of Each Section

cleaned = preprocess_section_text(

    sec.get(“raw_text”, “”),

    min_word_count=min_word_count_per_section,

    boilerplate_terms=boilerplate_terms

)

Each section’s text is cleaned and normalized, with removal of boilerplate phrases, extremely short fragments, or irrelevant patterns. This results in clean, semantically meaningful text suitable for analysis.

6. Chunking Using Hybrid Sentence-Aware Logic

chunks = hybrid_chunk_section(

    text=sec[“text”],

    max_tokens=max_tokens,

    min_tokens=min_tokens,

    sliding_overlap=sliding_overlap

)

The function converts each section into token-bounded chunks using the hybrid chunking mechanism. It preserves semantic coherence through sentence-based grouping while enforcing token constraints required for transformer models. Overlaps are used to reduce semantic loss between chunks.

7. Final Assembly of Structured Output

final_sections.append({

    “section_id”: sec[“id”],

    “section_heading”: sec[“heading”],

    “section_position”: sec[“position”],

    “chunk_id”: cid,

    “text”: chunk,

    “est_tokens”: est_tokens

})

Each chunk is stored with detailed metadata, including section identifiers, chunk indices, and estimated token counts. This standardized structure supports downstream tasks such as ranking explanations, semantic attribution, and model-based scoring.

Function: load_embedding_model

Overview

This function is responsible for loading a SentenceTransformer embedding model with built-in retry logic and automatic device selection. It ensures that the semantic pipeline always has a valid embedding model available—whether running on CPU or GPU.

The function is intentionally simple and self-contained: it chooses the appropriate device, attempts to load the model multiple times, logs each stage, and finally returns a ready-to-use embedding model with gradient computation disabled. Because this function performs a single, clear task without complex internal logic, its behavior is primarily explained within this overview, and a separate code-explanation subsection is not required.

It is also designed for robustness in production environments where model loading may occasionally fail due to network or system constraints. The retry mechanism ensures reliability across runs, which is crucial when this function serves as the foundation for downstream tasks like embedding generation, similarity search, and ranking-model interpretability.

Function: embed_texts

Overview

The embed_texts function generates sentence embeddings for a list of input texts using a loaded SentenceTransformer model. It provides a clean, efficient interface for converting raw text into numerical vector representations—an essential step for semantic similarity, clustering, ranking-explanation logic, and all downstream interpretation tasks in this project.

The function handles edge cases gracefully. If an empty list is provided, it returns a correctly shaped zero-array to maintain pipeline stability. For non-empty input, it embeds the texts in batches, ensuring efficiency when processing large volumes of content extracted from client pages. The output is always a NumPy array of shape (number_of_texts, embedding_dimension), making it easy to pass directly into distance/similarity computations.

Because the function is simple and self-contained, its behavior is fully described in the overview and does not require a separate Key Code Explanations subsection.

Function: load_spacy_model

Overview

The load_spacy_model function provides a lazy-loading mechanism for initializing a spaCy NLP model (default: “en_core_web_sm”). Lazy-loading ensures the model is loaded only once during runtime, improving performance and preventing repeated heavy initialization costs across multiple function calls in the pipeline.

The function also includes a safety check: if spaCy is not installed in the environment, it raises a clear, actionable error instead of failing silently. Once loaded, the model is cached in a global variable so that subsequent calls return the already-loaded model instantly. The spaCy pipeline is loaded with “textcat” disabled to keep the model lightweight and efficient for tasks like sentence segmentation and tokenization used elsewhere in the project.

Key Code Explanations

global _SPACY_MODEL

This instructs Python to use the module-level variable _SPACY_MODEL, enabling caching of the model to avoid repeated loads.

if _SPACY_MODEL is None:

This checks whether the model has already been loaded. If not, it proceeds to load it; otherwise, the previously loaded model is returned immediately.

if not _HAS_SPACY:

    raise RuntimeError(“spaCy not installed…”)

Before attempting to load the model, the function verifies that spaCy is available in the environment. If not, it raises a descriptive error prompting installation.

_SPACY_MODEL = spacy.load(model_name, disable=[“textcat”])

This loads the spaCy model while disabling the “textcat” component to reduce overhead since text classification isn’t required in this project.

Function: extract_entities

Overview

The extract_entities function performs Named Entity Recognition (NER) on a given text using spaCy. Its role within the project is to identify structured semantic signals such as people, organizations, locations, dates, and other named entities. Although entity extraction is not the primary focus of the expertise-depth evaluation, it provides valuable contextual enrichment that can support credibility cues, content structuring, or supplementary metadata analysis.

The function is intentionally lightweight and fails safely:

• If the input text is empty, it immediately returns an empty list.
• If spaCy is not available in the environment, it does not raise an error but gracefully returns an empty list.
• When available, it loads the cached spaCy model and extracts entities, returning them as (entity_text, entity_label) pairs.

Given the function’s simplicity and clarity, an additional code-explanation subsection is not necessary beyond the overview.

Key Code Explanations

if not text:

    return []

This prevents unnecessary processing by immediately returning an empty result when no text is provided.

if not _HAS_SPACY:

    return []

This ensures the function fails gracefully if spaCy is not installed, avoiding runtime errors during analysis.

nlp = load_spacy_model()

doc = nlp(text)

The cached spaCy model is loaded and applied to the text, creating a processed doc object containing token-level and entity-level annotations.

return [(ent.text, ent.label_) for ent in doc.ents]

This extracts all detected named entities and formats them as simple tuples, making them easy to integrate into downstream processing or reporting.

Function: cosine_sim

Overview

The cosine_sim function computes the cosine similarity between two embedding vectors. It is designed to be safe against invalid inputs by handling None values and zero-norm vectors. It returns a similarity value between 0.0 and 1.0, where higher values indicate more semantic closeness.

Because the function is short and self-explanatory, the essential logic is covered directly under the overview.

Key Code Explanations

denom = (np.linalg.norm(a) * np.linalg.norm(b))

This computes the denominator of the cosine similarity formula. If the product is zero, similarity cannot be computed.

return float(np.dot(a, b) / denom)

This applies the cosine similarity formula using the dot product divided by the magnitude product.

Function: tokenize_sentences

Overview

tokenize_sentences splits a block of text into individual sentences using NLTK’s sent_tokenize. If the text is empty, it simply returns an empty list. This function ensures consistent sentence-level processing across the project.

Since it is extremely simple, no additional code-explanation subsection is required.

Function: to_percent

Overview

This function converts a value in the 0 to 1 range into a percentage with one decimal precision. It also clamps out-of-range inputs to ensure the output lies strictly within 0 to 100.

No further internal explanation is needed due to its simplicity.

Function: normalize_score

Overview

normalize_score takes a numeric value and maps it into a 0–1 normalized range, applying boundaries at the minimum and maximum thresholds. It safely handles NaNs and out-of-range values. This function is used throughout the model to stabilize metrics before aggregation.

Key Code Explanations

if math.isnan(value):

    return 0.0

This prevents propagation of NaN values through downstream calculations.

return (value – min_val) / (max_val – min_val)

This applies the standard linear normalization formula once the value is in range.

Function: compute_semantic_depth

Overview

compute_semantic_depth is one of the important analytical functions in the pipeline. It evaluates how much conceptual layering, idea diversification, and semantic richness exists in a section of text. It does this by:

• Splitting the text into sentences
• Computing embeddings for each sentence
• Calculating the centroid of sentence embeddings
• Measuring dispersion of sentences around the centroid
• Measuring average pairwise similarity, which captures repetitiveness vs. diversity
• Combining these into a unified depth_score (0–100)

Higher depth typically means the section covers multiple sub-concepts, offers richer explanations, or brings additional expert-level contextualization.

Key Code Explanations

embeddings = embed_texts(model, sentences, batch_size)

centroid = np.mean(embeddings, axis=0)

The embeddings for all sentences are generated, and their centroid is calculated. The centroid represents the semantic “center” of the section.

dists = np.linalg.norm(embeddings – centroid, axis=1)

dispersion = float(np.mean(dists))

This measures how far each sentence embedding lies from the centroid. Higher dispersion means more conceptual variety.

avg_pairwise_sim = float(np.mean(sims)) if sims else 1.0

This computes the average similarity between all possible sentence pairs. If sentences are too similar, the content likely lacks depth.

norm_disp = normalize_score(dispersion, 0.0, scale_dispersion)

norm_sim = 1.0 – normalize_score(avg_pairwise_sim, 0.0, scale_sim)

Dispersion is normalized directly; similarity is inverted because lower similarity indicates deeper, more varied content.

depth_raw = 0.6 * norm_disp + 0.4 * norm_sim

A weighted combination forms the raw depth indicator. Dispersion receives a slightly higher weight because conceptual diversity is a stronger depth signal.

“depth_score”: to_percent(depth_score)

The final normalized score is converted to a 0–100 scale for client-facing interpretability.

Function: _safe_lower_tokens

Overview

_safe_lower_tokens performs lightweight, safe tokenization by lowercasing the text and extracting alphanumeric tokens using a regex. It avoids heavy NLP tokenizers and provides consistent token lists for frequency-based computations, terminology checks, and other heuristic analyses.

This function ensures minimal preprocessing overhead while maintaining clean, usable tokens for downstream calculations such as terminology precision.

Because the function is small and straightforward, no separate code-explanation subsection is needed.

Function: _safe_token_list

Overview

This helper function simply wraps _safe_lower_tokens and returns the same cleaned, lowercased list of tokens. It enhances readability across the codebase by providing a standard entry point for safe tokenization.

No additional explanation is required due to its simplicity.

Function: compute_terminology_precision

Overview

compute_terminology_precision evaluates how accurately a section of content uses domain-specific terminology. This helps quantify whether a page demonstrates subject-matter accuracy, expert vocabulary, and conceptual correctness—key components of expertise-oriented evaluation.

The function supports two modes:

1.    Domain terms provided

• Matches canonical expert terms directly or via simple fuzzy checks.
• Calculates ratio of found vs. expected terms.
• Applies penalties if the text overuses vague, generic words.
• Returns precision-related metrics and missing terminology.

2.    No domain terms provided

• Automatically extracts high-frequency, non-vague tokens as terminology candidates.
• Produces a very lightweight proxy score that reflects terminology richness.

This mechanism ensures the project can analyze terminology quality even when explicit domain dictionaries are not available.

Key Code Explanations

1. Lightweight Token Generation

tokens = _safe_token_list(section_text)

total_tokens = max(1, len(tokens))

Generates a cleaned token list to support frequency heuristics. max(1, …) prevents division-by-zero errors when calculating ratios.

2. Exact and Fuzzy Matching for Domain Terms

if t in section_text.lower():

    found_terms.append(t)

This checks for direct, exact presence of the domain term in the text.

if any(t in ” “.join(tokens[i:i+len(t.split())]) for i in range(len(tokens))):

This provides a simple n-gram–style fuzzy matching to detect multi-word terms or slight structural variations in terminology.

3. Vague Word Penalty

vague_hits = sum(1 for tok in tokens if tok in VAGUE_WORDS)

vague_penalty = min(0.5, (vague_hits / total_tokens))

This penalizes terminology precision if many vague, generic tokens appear in the text (e.g., thing, stuff, various, multiple). The penalty is capped at 0.5 to avoid excessive suppression.

4. Precision Score Normalization

precision_score = normalize_score(precision, 0.0, 1.0)

Maps the computed precision value into a stable 0–1 range so it can be converted into a 0–100 score later. Normalization also protects against out-of-range values introduced by vague-word penalties.

5. Auto-Term Detection (Fallback Mode)

freq = {}

for tok in tokens:

    if len(tok) <= 2:

        continue

    if tok in VAGUE_WORDS:

        continue

    freq[tok] = freq.get(tok, 0) + 1

When no domain terms are provided, the function identifies potential terminology by selecting frequent, non-vague, meaningful tokens of reasonable length.

6. Candidate-Term Ranking

candidates = sorted(freq.items(), key=lambda x: (-x[1], x[0]))[:top_n_candidates]

This sorts candidate tokens by:

1. Descending frequency, to highlight likely domain-specific terms.
2. Alphabetical order as a tie-breaker. Only the top N candidates are retained.

Function: count_matches

Overview

The count_matches function provides a lightweight mechanism to detect how frequently specific linguistic patterns appear in a given text. By performing case-insensitive matching and intelligently switching between strict word-boundary matches (for single-word patterns) and substring-based matches (for multi-word expressions), the function ensures more accurate detection of meaningful semantic signals. This capability is essential for measuring higher-level reasoning structures because many reasoning cues—such as causal markers, comparative phrases, or conditional connectors—rely on consistent textual patterns.

The function handles empty text gracefully, normalizes the text by converting it to lowercase, and then iterates through all patterns to accumulate the total number of matches. It remains simple, efficient, and fully deterministic, making it ideal for fast linguistic signal detection within large-scale SEO or semantic-analysis pipelines.

Function: compute_reasoning_structure

Overview

The compute_reasoning_structure function quantifies how well a content section demonstrates structured reasoning. It analyzes the presence of different categories of reasoning signals—causal, conditional, procedural, comparative, and examples—using pre-defined linguistic patterns. By counting how often such signals surface in the text, the function translates qualitative reasoning strength into numerical indicators that can be compared across sections or pages.

This function is crucial for understanding whether a page explains concepts logically, supports claims, introduces comparisons, and provides actionable steps or examples. After collecting raw counts for each reasoning type, it computes a normalized reasoning quality score. The normalization caps the impact of excessive repetition and boosts the score slightly if the text includes examples, since examples strongly enhance clarity and authority. The output is a structured dictionary summarizing reasoning depth and balance.

Key Code Explanations

text_lower = section_text.lower()

This converts the entire section text to lowercase to ensure uniform, case-insensitive matching. Since reasoning markers may appear in different cases depending on formatting or sentence location, lowering the text avoids missing signals due to capitalization differences.

causal = count_matches(text_lower, CAUSAL_PATTERNS)

This line uses the count_matches helper to search for all predefined causal reasoning markers such as because, therefore, or as a result. The result represents how many times the section expresses cause-and-effect logic, which is a cornerstone of expert reasoning.

total_signals = causal + conditional + procedural + comparative + example_count

Here, the function aggregates raw counts from all reasoning categories. This combined signal strength acts as the basis for scaling the reasoning quality score. The logic assumes that richer reasoning emerges from the presence of multiple types of reasoning cues rather than a singular pattern repeated excessively.

reasoning_norm = normalize_score(total_signals, 0.0, 6.0)

This applies a normalization function that maps the total reasoning signals into a 0–1 range, using a cap of 6 signals. The cap prevents over-inflation of the score if a section repeatedly uses the same patterns while still ensuring that even moderately reasoned content receives meaningful credit.

if example_count > 0:

    reasoning_norm = min(1.0, reasoning_norm + 0.10)

Examples significantly improve semantic clarity, so this conditional block boosts the final reasoning score slightly when example markers are detected. The score is clipped at 1.0 to avoid exceeding the maximum normalization threshold.

Function: compute_contextual_richness

Overview

The compute_contextual_richness function evaluates how much real-world, contextual, and example-driven substance a content section provides. Contextual richness reflects how well a piece of text grounds its explanations using concrete entities, diverse references, real examples, and conceptual variety. These elements are essential markers of authoritative, experience-backed content because they demonstrate specificity rather than abstract or generic descriptions.

This function measures contextual depth across four dimensions:

1. Explicit examples — Clear signals that the content provides illustrations or demonstrations.
2. Named entity density — Presence of people, concepts, organizations, locations, technologies, etc.
3. Entity type diversity — Range of different semantic categories mentioned (e.g., PERSON, ORG, PRODUCT, EVENT).
4. Semantic diversity of entity mentions — An optional embedding-based metric that estimates how conceptually varied the referenced entities are.

By combining all four indicators using a weighted aggregation, the function produces a contextual richness score from 0 to 100. Sections with real examples, meaningful references, and diverse concepts score higher, indicating stronger E-E-A-T alignment and deeper informational value.

Key Code Explanations

ex_count = count_matches(section_text, EXAMPLE_PATTERNS)

This identifies how many explicit example cues appear in the text (such as for example, such as, for instance). Examples are strong signals of contextual grounding, and this count contributes significantly to the richness score.

entities = extract_entities(section_text) if _HAS_SPACY else []

Named entities represent meaningful, real-world references. If spaCy is available, the function extracts these entities from the text. Otherwise, it defaults to an empty list to ensure the pipeline remains operational even without spaCy.

label_diversity = len(set([lbl for _, lbl in entities])) if entities else 0

This computes how many different types of entities appear, such as PERSON, ORG, GPE, PRODUCT, etc. A higher variety of entity labels indicates conceptually richer and broader content, contributing positively to contextual depth.

if model and entities:

    ent_texts = [e for e, _ in entities]

If an embedding model is provided, the function prepares entity text strings for embedding. Using embeddings allows semantic analysis beyond simple counts, helping determine how varied the referenced entities are conceptually.

ent_emb = embed_texts(model, ent_texts, batch_size)

This line computes vector embeddings for each entity mention. These embeddings capture semantic meaning and enable the later estimation of conceptual variance among entities.

cent = np.mean(ent_emb, axis=0)

dists = np.linalg.norm(ent_emb – cent, axis=1)

diversity_score = float(np.mean(dists))

Here, the function computes the average semantic distance of entity embeddings from their centroid. If entities vary widely in concept, the average distance increases. This acts as a continuous measure of semantic diversity rather than just label-based diversity.

ex_norm = normalize_score(ex_count, 0.0, 3.0)

ent_count_norm = normalize_score(entity_count, 0.0, 6.0)

label_norm = normalize_score(label_diversity, 0.0, 4.0)

emb_norm = normalize_score(diversity_score, 0.0, 0.6)

Each component is normalized to a 0–1 range using carefully chosen caps. These reflect real-world expectations: examples beyond three add limited additional value, entity counts beyond six saturate, and embedding diversity rarely exceeds 0.6 for this use case.

raw = 0.35 * ex_norm + 0.35 * ent_count_norm + 0.2 * label_norm + 0.1 * emb_norm

This weighted formula defines the final contextual richness score. Examples and entity counts receive the highest weights due to their strong contribution to perceived depth. Entity label diversity and semantic diversity refine this score but play a secondary role.

Function: compute_confidence_clarity

Overview

The compute_confidence_clarity function estimates how confidently and clearly a section is written by analyzing the distribution of hedge words and assertive words. Hedge words reduce commitment (“might”, “possibly”, “could”), while assertive words project clarity and authority (“clearly”, “definitely”, “shows”).

This metric is important because content that signals confidence often correlates with expertise. Conversely, excessive hedging can make content feel uncertain or weak. The function tokenizes the section, counts these linguistic markers, computes their ratios relative to total words, and converts them into a normalized 0–100 score.

The scoring logic rewards sections with more assertive markers and fewer hedging cues. This balance reflects how confidently ideas are expressed, which is a key component of expertise depth and credibility. The function also returns the raw counts and ratios for transparency, allowing deeper debugging or interpretability.

Key Code Explanations

tokens = _safe_lower_tokens(section_text)

This produces a clean, lowercased list of tokens using the safe tokenizer. It avoids punctuation noise and ensures consistent matching against predefined word lists.

hedge_hits = sum(1 for t in tokens if t in HEDGE_WORDS)

assertive_hits = sum(1 for t in tokens if t in ASSERTIVE_WORDS)

These lines count how many hedge and assertive words appear in the section. The counts serve as the foundation for determining the overall confidence/clarity signal.

raw = max(0.0, assertive_ratio – hedge_ratio + 0.2)

This line computes the initial score.

• If assertive_ratio is higher than hedge_ratio, the score increases.
• If hedge_ratio dominates, the score decreases.
• A small constant bias (+ 0.2) prevents the score from collapsing to zero in neutral cases and ensures smoother scaling across different text lengths.

norm = normalize_score(raw, 0.0, 0.5)

The raw value is mapped into a 0–1 range using the project’s standard normalization method. The upper bound (0.5) is chosen to keep the signal moderate and avoid over-amplifying small differences in language style.

return {

    “confidence_clarity_score”: to_percent(norm),

    “hedge_hits”: hedge_hits,

    “assertive_hits”: assertive_hits,

    “hedge_ratio”: round(hedge_ratio, 3),

    “assertive_ratio”: round(assertive_ratio, 3)

}

The function returns both the final confidence/clarity score (converted to 0–100) and the underlying evidence. Clients and downstream systems can rely on this structured output for deeper interpretability or reporting.

Function: compute_section_signals

Overview

The compute_section_signals function acts as the central aggregator for all feature evaluations performed on a single content section. While other functions compute individual metrics—such as semantic depth, terminology precision, reasoning quality, contextual richness, and confidence/clarity—this function orchestrates them into one unified results dictionary.

This unified representation is extremely valuable for downstream analysis, reporting, or ranking. Instead of handling multiple independent outputs, the client receives a structured set of feature signals, each with its own score and internal evidence. This design also ensures modularity: every sub-feature can evolve independently, while the top-level function provides a stable interface for the entire project pipeline.

The function takes in the section text, the embedding model, the optional domain term list, and relevant parameters for pre-processing or scoring. It then executes each individual scoring function in a logical sequence and compiles their outputs into a single consolidated dictionary.

Key Code Explanations

depth = compute_semantic_depth(section_text, model, min_sentences_for_depth, batch_size)

This computes the semantic depth score, which assesses the conceptual layering and structural variability within the section. It uses embeddings and sentence-level analysis to determine how deeply the content explores the topic.

term = compute_terminology_precision(section_text, domain_terms, model, top_n_candidates)

This triggers the terminology precision evaluation. It checks how effectively the section uses domain-relevant terms, identifies missing terminology, and measures vague language frequency. This is crucial for detecting topic authority and terminology accuracy.

reasoning = compute_reasoning_structure(section_text)

The function identifies reasoning cues (causal, conditional, procedural, comparative). This helps quantify the logical structure and instructional clarity of the section, key markers of high-quality explanatory content.

contextual = compute_contextual_richness(section_text, model=model, batch_size=batch_size)

This computes contextual richness using entities, examples, entity diversity, and optional semantic variance. It reflects how well the section uses grounded, real-world references and supporting examples.

confidence = compute_confidence_clarity(section_text)

This calculates confidence/clarity signals by identifying linguistic markers that reflect writer certainty, specificity, and clarity of explanation. It is especially important for determining whether content feels authoritative and trustworthy.

signals = {

    “depth”: depth,

    “terminology”: term,

    “reasoning”: reasoning,

    “contextual”: contextual,

    “confidence”: confidence

}

This assembles all computed feature components into a single structured dictionary. Each key contains both a score (0–100) and its internal evidence, enabling rich downstream diagnostics and easy integration into reporting systems.

Function: aggregate_expertise_score

Overview

The aggregate_expertise_score function consolidates multiple feature signals—depth, terminology precision, reasoning quality, contextual richness, and confidence/clarity—into a single, interpretable Expertise Depth Score on a 0–100 scale. This score reflects the overall content authority and semantic expertise present in a section or page, helping clients quickly assess the quality and reliability of their content.

Each feature contributes to the overall score based on pre-defined or custom weights, allowing flexibility for different client priorities. For example, technical content might prioritize depth and terminology, while thought-leadership articles might emphasize reasoning and contextual richness. The function also provides the per-component scores alongside the overall score for transparency, enabling clients to see which aspects of expertise are strong or need improvement.

Key Code Explanations

Line:

if weights is None:

    weights = DEFAULT_WEIGHTS

This sets the default weighting scheme if the client does not provide custom weights. The defaults emphasize depth (30%), terminology (20%), reasoning (20%), contextual richness (15%), and confidence/clarity (15%), reflecting typical importance distribution for content authority evaluation.

depth_score = signals[“depth”].get(“depth_score”, 0.0) / 100.0

terminology_score = signals[“terminology”].get(“terminology_precision_score”, 0.0) / 100.0

reasoning_score = signals[“reasoning”].get(“reasoning_quality_score”, 0.0) / 100.0

contextual_score = signals[“contextual”].get(“contextual_richness_score”, 0.0) / 100.0

confidence_score = signals[“confidence”].get(“confidence_clarity_score”, 0.0) / 100.0

These lines safely extract the normalized component scores from the section signals dictionary. Each score is converted from a 0–100 scale to 0–1 for consistent weighted aggregation, ensuring that missing or undefined signals default to zero rather than causing errors.

overall = (

    depth_score * weights.get(“depth”, 0.0)

    + terminology_score * weights.get(“terminology”, 0.0)

    + reasoning_score * weights.get(“reasoning”, 0.0)

    + contextual_score * weights.get(“contextual”, 0.0)

    + confidence_score * weights.get(“confidence”, 0.0)

)

This performs the weighted aggregation of all component scores. Each normalized score is multiplied by its respective weight and summed to compute a single unified measure of expertise depth. The approach allows flexible customization while preserving interpretability.

return {

    “overall_expertise_score”: round(overall * 100.0, 1),

    “components”: {

        “depth”: round(depth_score * 100.0, 1),

        “terminology”: round(terminology_score * 100.0, 1),

        “reasoning”: round(reasoning_score * 100.0, 1),

        “contextual”: round(contextual_score * 100.0, 1),

        “confidence”: round(confidence_score * 100.0, 1)

    },

}

The function returns both the overall score (0–100) and the rounded component scores, maintaining clarity and transparency. Clients can use the component breakdown to understand which dimensions of expertise are strong or require improvement, making the output actionable for content audits or strategy planning.

Function: evaluate_page_expertise

Overview

The evaluate_page_expertise function serves as the top-level orchestrator for computing semantic expertise signals across all sections of a webpage. It integrates the modular functionality of section-level analysis, including semantic depth, terminology precision, reasoning structure, contextual richness, and confidence/clarity, and then aggregates these signals into an overall Expertise Depth Score for each section.

By iterating through each section of the page, the function ensures that even large or multi-topic pages are evaluated in a granular, section-wise manner. This allows clients to identify specific content blocks that demonstrate strong expertise versus areas that may require improvement. The function also supports optional domain-specific terminology, batch processing for embeddings, and customizable feature weighting, providing flexibility for various types of web content.

Key Code Explanations

for section in page_data.get(“sections”, []):

This line iterates over all sections extracted from the webpage. Using get(“sections”, []) ensures that the function gracefully handles pages with missing or empty sections, avoiding runtime errors.

signals = compute_section_signals(

    section_text=text,

    model=model,

    min_sentences_for_depth=min_sentences_for_depth,

    batch_size=batch_size,

    domain_terms=domain_terms,

    top_n_candidates=top_n_candidates

)

Here, the function computes all feature signals for a single section using the modular compute_section_signals function. This includes depth, terminology, reasoning, contextual richness, and confidence/clarity, providing a comprehensive semantic assessment of the section’s content.

aggregate = aggregate_expertise_score(signals, weights=weights)

This aggregates the per-feature signals into a single Expertise Depth Score for the section, applying either default or user-provided weights. This step converts the multi-dimensional analysis into a concise, interpretable metric.

section[“signals”] = signals

section[“aggregate_score”] = aggregate

The computed feature signals and aggregated score are stored back into the section dictionary. This design keeps all evidence and results tied to each section, enabling clients to perform detailed content audits and understand why a section scored high or low in expertise.

return page_data

Finally, the function returns the complete page data with all sections annotated with semantic expertise signals and aggregated scores. This output can be used directly for client reporting, visualization, or further analysis.

Function: detect_expertise_gaps

Overview

The detect_expertise_gaps function identifies areas within a section of content where semantic expertise signals are weak or missing. Using both the aggregated expertise scores and the raw feature signals, it checks for deficiencies across five key dimensions: semantic depth, terminology precision, reasoning structure, contextual richness, and confidence/authority tone. The output is a list of descriptive labels representing specific gaps in expertise, such as low_semantic_depth or missing_domain_terms.

This function is valuable for content audits, as it provides actionable insights at the section level. Clients can directly use these gap labels to prioritize improvements, such as enhancing domain-specific terminology, adding examples, or improving clarity and confidence in writing. By combining both quantitative scores and qualitative signals, the function offers a balanced assessment of where content falls short in demonstrating expertise.

Key Code Explanations

depth = comp.get(“depth”, 0)

terminology = comp.get(“terminology”, 0)

reasoning = comp.get(“reasoning”, 0)

contextual = comp.get(“contextual”, 0)

confidence = comp.get(“confidence”, 0)

These lines extract the aggregated per-component scores from the section. Using .get(…, 0) ensures that missing components default to zero, preventing errors while allowing the function to operate even if some signals are absent.

if depth < 40:

    gaps.append(“low_semantic_depth”)

if sig.get(“depth”, {}).get(“dispersion”, 1) < 0.15:

    gaps.append(“low_conceptual_variety”)

These lines evaluate semantic depth. The first condition flags overall low depth based on the aggregated score, while the second condition looks at the internal dispersion signal, which measures conceptual variety among sentences. Low dispersion indicates that the content may be repetitive or narrowly focused.

if terminology < 60:

    gaps.append(“weak_domain_terminology”)

if len(sig.get(“terminology”, {}).get(“missing_terms”, [])) > 0:

    gaps.append(“missing_domain_terms”)

This block checks terminology precision. Sections scoring below 60 are considered weak in domain terminology, and any missing terms from the pre-defined domain set are flagged as missing_domain_terms, highlighting gaps in critical vocabulary coverage.

if reasoning < 40:

    gaps.append(“weak_reasoning_structure”)

if sig.get(“reasoning”, {}).get(“example_count”, 0) == 0:

    gaps.append(“missing_examples”)

Here, reasoning quality is assessed. Low reasoning scores indicate insufficient logical or structured argumentation, and sections with no examples are flagged to highlight missing evidence or illustrative content.

if contextual < 35:

    gaps.append(“low_contextual_richness”)

if sig.get(“contextual”, {}).get(“entity_count”, 0) <= 1:

    gaps.append(“low_entity_diversity”)

This block evaluates contextual richness. Low scores or a lack of named entities suggest that the content may be shallow or lacking relevant context, signaling an opportunity to enrich the section with examples, analogies, or diverse entities.

if hedge_hits > 3:

    gaps.append(“excessive_hedging”)

if assertive_hits == 0:

    gaps.append(“low_expert_assertiveness”)

Finally, the function assesses confidence and authority in writing. Excessive hedging or a complete absence of assertive statements is flagged to indicate that the content may lack authoritative or expert tone.

Function: generate_recommendations

Overview

The generate_recommendations function translates detected expertise gaps into actionable, expert-oriented content improvement suggestions. It takes a list of gap labels, typically generated by the detect_expertise_gaps function, and maps each label to a deterministic, pre-defined recommendation. The output is a list of human-readable guidance statements that are immediately usable by content authors or SEO specialists to enhance a section’s depth, terminology, reasoning, contextual richness, and confidence.

This function provides practical value by bridging the gap between automated content analysis and concrete actions. For example, if a section is flagged with low_semantic_depth, the function will suggest adding deeper conceptual explanations; if missing_domain_terms is detected, it recommends incorporating key industry terminology. Its rule-based approach ensures predictable and consistent recommendations across sections.

Key Code Explanations

suggestions = [mapping[g] for g in gaps if g in mapping]

This line generates the final list of recommendations by iterating over the input gap labels and selecting the corresponding suggestions from the mapping. The conditional check if g in mapping ensures that only recognized gaps are converted to recommendations, avoiding errors from unexpected or unknown labels.

Function: apply_authority_analysis

Overview

The apply_authority_analysis function is a high-level wrapper that integrates expertise signal evaluation, gap detection, and actionable recommendation generation for a webpage. It processes each section in the page_data dictionary, detects gaps in expertise using previously computed signals, and then maps those gaps to expert-oriented improvement recommendations. The function enriches the original page_data by injecting two new fields per section: expertise_gaps and gap_improvement_recommendations.

This function serves as the final step in the authority and expertise assessment workflow, combining automated analysis with actionable guidance for content improvement. Sections without text are handled gracefully by assigning empty lists to both fields, ensuring robustness across varied page structures.

Key Code Explanations

gaps = detect_expertise_gaps(section)

recs = generate_recommendations(gaps)

These lines are the core of the function’s processing. First, detect_expertise_gaps(section) analyzes the section’s signals and identifies missing or weak expertise markers. Then, generate_recommendations(gaps) converts these detected gaps into practical, expert-focused improvement suggestions. This separation of detection and recommendation ensures modularity and clarity in the content evaluation workflow.

section[“expertise_gaps”] = gaps

section[“gap_improvement_recommendations”] = recs

These lines update the original page_data structure by injecting the analysis results directly into each section. This allows downstream processes or client-facing reports to access both the identified gaps and actionable recommendations in a structured format, keeping the data self-contained and immediately usable.

Function: build_scorecard

Overview

The build_scorecard function creates a client-friendly summary of a page’s expertise analysis. It aggregates section-level expertise scores into descriptive statistics and identifies the strongest and weakest sections for quick insights. It also computes component-level statistics (mean and median) and flags systemic or inconsistent weaknesses across the page. The result is stored in the page_data dictionary under the scorecard key, making it easy to generate reports or dashboards.

This function focuses on summarization rather than detailed per-section analysis, providing a concise overview of both strengths and weaknesses.

Key Code Explanations

scores = [s[“aggregate_score”][“overall_expertise_score”] for s in sections]

components = [“depth”, “terminology”, “reasoning”, “contextual”, “confidence”]

comp_values = {c: [s[“aggregate_score”][“components”][c] for s in sections] for c in components}

These lines collect overall expertise scores and individual component scores for all sections. scores stores each section’s overall expertise score, while comp_values organizes per-component scores into separate lists. This structure allows subsequent statistical calculations and percentile-based gap analysis.

strongest = sorted(sections, key=lambda s: s.get(“aggregate_score”, {}).get(“overall_expertise_score”), reverse=True)[:5]

weakest = sorted(sections, key=lambda s: s.get(“aggregate_score”, {}).get(“overall_expertise_score”))[:5]

Here, the function identifies the top 5 and bottom 5 sections based on overall expertise. Sorting the sections by their scores provides a quick reference to areas of high quality and areas needing improvement, which is essential for actionable reporting.

systemic_low = [c for c, v in comp_values.items() if np.mean(v) < 40]

inconsistent = [c for c, v in comp_values.items() if np.var(v) > 800]

These lines detect page-level gaps. systemic_low flags components with low average scores across the page, indicating widespread weaknesses. inconsistent identifies components with high variance, pointing to uneven content quality across sections. This helps guide strategic improvements at the page level rather than just per section.

Function: detect_page_level_authority_gaps

Overview

The detect_page_level_authority_gaps function evaluates the overall expertise quality of a page by analyzing component-level scores across all sections. It detects systemic weaknesses, inconsistencies, and dominance patterns in key content attributes such as depth, terminology, reasoning, contextual richness, and confidence. The function generates a structured set of flags and metrics, stored in the authority_gaps key of the page_data dictionary, which highlights areas where the page may lack authority or exhibit uneven quality. This allows clients to identify both widespread and localized content issues that impact perceived expertise.

Key Code Explanations

global_low_components = [

    c for c, vals in comp_values.items()

    if vals and np.mean(vals) < 40

]

These lines identify components with systemic weaknesses across the page. By checking the mean score for each component and flagging those below 40, the function detects areas where the content consistently underperforms, such as low semantic depth or weak domain terminology.

high_variance_components = [

    c for c, var in variance_flags.items()

    if var is not None and var > 800

]

This segment detects components with high score variability. High variance indicates inconsistent treatment of a component across sections—for example, some sections may be very strong in reasoning while others are weak. This helps clients identify content areas that lack uniform expertise.

distribution_profile[c] = {

    “low_ratio”: float(low_ratio),

    “count_low”: int(sum(v < 40 for v in vals)),

    “count_total”: len(vals),

    “systemic_flag”: low_ratio >= 0.5

}

Here, the function calculates a detailed distribution profile for each component, including the proportion of sections scoring below 40. The systemic_flag signals when at least half of the sections are underperforming, providing a clear indicator of persistent weaknesses at the page level.

consistency_flags[c] = {

    “dominance_ratio”: float(dominance_ratio),

    “dominance_flag”: dominance_ratio > 0.5

}

These lines assess dominance within a component: if the top section contributes disproportionately to the page’s overall score (>50%), it suggests that expertise is concentrated in a single section rather than distributed evenly. This flags potential risks where a page might appear expert in some areas but lacks holistic authority.

Function: generate_section_recommendations

Overview

The generate_section_recommendations function creates actionable, structured guidance for improving a single section of content based on its expertise signals and aggregate score. It evaluates strengths, weaknesses, and missing indicators, then assigns a priority level (low, medium, or high) based on the overall expertise score. The recommendations are categorized into issues, strengths, missing_signals, and action_categories, enabling clients to quickly understand which areas of the section require attention and what type of interventions to apply.

Key Code Explanations

if overall >= 70:

    recommendations[“priority_level”] = “low”

elif 40 <= overall < 70:

    recommendations[“priority_level”] = “medium”

else:

    recommendations[“priority_level”] = “high”

These lines determine the section’s priority for improvement based on the aggregate expertise score. High-scoring sections (>70) require minimal attention, mid-range sections (40–70) are medium priority, and low-scoring sections (<40) are flagged as high priority. This allows clients to focus effort where it is most needed.

if comps.get(“depth”, 0) < 40:

    recommendations[“issues”].append(“low_semantic_depth”)

    recommendations[“action_categories”].append(“increase_depth”)

This logic detects sections with shallow semantic content and assigns a targeted improvement action. Similar conditional checks exist for terminology, reasoning, contextual richness, and confidence, mapping low component scores to corresponding actionable categories.

reasoning_s = signals.get(“reasoning”, {})

if reasoning_s.get(“example_count”, 0) == 0:

    recommendations[“missing_signals”].append(“missing_examples”)

Here, the function identifies missing or underrepresented evidence signals, such as absent examples, causal reasoning, or procedural steps. These checks highlight gaps in reasoning and content support that might not be captured purely by numeric scores.

recommendations[“action_categories”] = list(set(recommendations[“action_categories”]))

This line deduplicates the action categories, ensuring that each suggested action appears only once, resulting in a concise, client-friendly list of interventions.

Function: generate_page_recommendations

Overview

The generate_page_recommendations function iterates over all sections of a page and attaches the structured recommendations generated by generate_section_recommendations to each section. It ensures that every section has a consistent, actionable guidance structure. Sections missing aggregate scores are initialized with empty recommendations, maintaining a clean and predictable output format for client consumption. This function provides a full-page view of actionable insights for enhancing overall expertise and authority.

Function: display_results

Overview

The display_results function provides a professional, human-readable summary of the Expertise Depth Evaluator results for multiple pages. It is designed as a reporting utility that presents page-level and section-level insights in a compact, user-friendly format. For each page, it prints the URL and title, followed by an overview of page-level metrics such as the mean and median overall expertise scores. It also highlights systemic gaps, including components that are consistently low across sections or exhibit high variance, helping users quickly identify areas of concern at the page level.

For section-level insights, the function displays the strongest and weakest sections based on aggregate expertise scores, showing a truncated preview of section text and a summary of component scores. It further provides a detailed section-level highlights view, sorted by score, including component breakdowns, detected expertise gaps, and actionable recommendations. The function handles missing data safely, ensuring that even incomplete or partially processed pages can be displayed without errors. By combining scorecards, gap detection, and improvement guidance into a concise report, this function enables users to quickly assess content strengths, weaknesses, and prioritized actions for enhancing expertise depth.

Result Analysis and Explanation

Page-Level Expertise Overview

The overall expertise of the page is moderate, with a mean score of 47.5 and a median of 49.3. These scores indicate that while certain sections demonstrate good expert-level content, there is significant room for improvement across the page. The overall range suggests that only a few sections reach high expertise, while others are relatively weak.

Two components are identified as systemically low across the page: reasoning and contextual richness. This indicates that the page generally lacks well-structured cause-and-effect explanations and sufficient contextual grounding, such as diverse examples or entity references. No components show high variance, implying that while reasoning and contextual richness are weak, these weaknesses are consistently present rather than being uneven across sections.

Overall, the page shows foundational expertise in certain areas like terminology, but the low systemic components highlight opportunities to strengthen logical structuring and richer contextual references.

Strongest Sections

The top three sections demonstrate the highest levels of expertise on the page.

1.    Section 1 (Score: 75.4) focuses on monitoring canonical headers using Google Search Console. It achieves a perfect terminology and reasoning score, indicating precise use of domain-specific terms and strong logical instructions. Depth is reasonably high at 68, and contextual richness is 60, suggesting some examples and diversity of concepts are present. The confidence score is lower at 40, highlighting a need for more authoritative phrasing. Key gaps include missing examples and low assertiveness, which can be addressed by incorporating real-world scenarios and clearer expert statements.

2.    Section 2 (Score: 71.6) provides a detailed step-by-step guide for verifying canonical headers using Developer Tools. Terminology is again perfect, and contextual richness is strong at 83.3, reflecting clear references and applied guidance. Reasoning is moderate at 60, indicating partial structure in explanations. The confidence score is 40, and excessive hedging was detected. The recommendation focuses on reducing overly cautious phrasing to enhance authoritative tone.

3.    Section 3 (Score: 65.8) emphasizes testing headers and avoiding conflicting directives. Depth is strong at 75.3, and terminology is perfect. However, reasoning is weak at 26.7, suggesting that logical connections, cause-effect explanations, or step-by-step procedures are insufficiently articulated. Contextual richness is relatively high at 76.7, and confidence is 42.3. The key recommendation is to improve reasoning by explicitly adding structured explanations or comparisons.

These sections show that high terminology usage and some contextual richness contribute strongly to overall expertise scores, but reasoning and confidence remain limiting factors even in the top-performing content.

Weakest Sections

The three weakest sections have scores below 20, indicating minimal contribution to overall expertise. These sections consist largely of raw code snippets or header configuration examples without accompanying explanations.

• Scores range from 15.6 to 19.6, reflecting poor depth, reasoning, and confidence.
• The content is factual but lacks guidance, applied examples, or expert-level context.
• Terminology is present in raw form but does not contribute meaningfully to semantic depth or clarity.

These weak sections highlight that code-only content or overly technical listings without explanatory context significantly lower the page’s aggregate expertise. Improving these sections requires adding conceptual explanations, illustrative examples, and structured reasoning.

Section-Level Highlights

A closer look at the top three sections reveals the interplay between various expertise components:

• Depth reflects the complexity and conceptual coverage of a section. Sections with higher depth scores provide comprehensive explanations of why and how certain headers work.
• Terminology consistently scores 100 in the top sections, indicating precise and correct use of domain-specific terms. This is a key strength of the page.
• Reasoning varies significantly. Section 1 achieves perfect reasoning by linking steps to expected outcomes, Section 2 is moderately structured, and Section 3 suffers due to missing cause-effect explanations.
• Contextual richness captures the presence of examples, analogies, and entity diversity. Sections 2 and 3 have higher scores, suggesting good inclusion of applied context, while Section 1 could add more illustrative examples.
• Confidence is consistently lower than other metrics, indicating that the content often uses hedging or lacks authoritative phrasing.

Key gaps across these sections are:

• Missing examples in Section 1
• Excessive hedging in Section 2
• Weak reasoning structure in Section 3

Recommendations for improvement include:

• Incorporating real-world examples or scenarios to demonstrate applied understanding.
• Using authoritative language to convey confidence and expert knowledge.
• Adding structured reasoning, such as cause-effect explanations or step-by-step guidance, particularly in sections with low reasoning scores.

Overall Interpretation

The page demonstrates strengths in domain terminology and moderate depth in top sections, but systemic weaknesses in reasoning and contextual richness prevent it from achieving high overall expertise. Confidence levels are also low, reflecting hedging or lack of assertiveness in presenting information. Strong sections show that high terminology and clear instructions can produce scores above 70, but weaker sections pull the average down, and code-only sections score poorly in depth and reasoning.

The results highlight clear opportunities for improvement: enriching reasoning structure, embedding illustrative examples, and enhancing assertive, authoritative expression across all sections. Addressing these gaps would significantly raise both the mean and median expertise scores.

Result Analysis and Explanation

This section provides a comprehensive analysis of content expertise, structural quality, and authority signals across multiple pages. Each aspect is presented in a professional, practical, and SEO-focused manner, with clear guidance on interpretation, thresholds, and actionable insights. Visualizations are also explained to provide an intuitive understanding of scores, gaps, and consistency.

Page-Level Expertise Assessment

Overall Expertise

Overall expertise is calculated as a combination of multiple components including depth, terminology, reasoning, contextual relevance, and confidence. It represents the aggregate ability of the page to demonstrate authoritative, actionable, and semantically rich content.

·         Threshold Interpretation:

• Above 70: High expertise – content is robust, actionable, and demonstrates authoritative knowledge.
• 50–70: Medium expertise – content is generally solid but may have minor gaps in reasoning, contextual examples, or clarity.
• Below 50: Low expertise – content lacks depth, may be inconsistent, and is likely to underperform in establishing authority.

The analyzed pages show a range of overall mean scores from approximately 48 to 58. Scores in the upper 50s suggest reasonable expertise coverage, though pages with scores below 50 indicate opportunities to enhance semantic depth and reasoning.

Systemic Low Components

Systemic low components highlight areas where a page consistently underperforms across multiple sections. These typically include reasoning and contextual support. Repeated low scores in these areas can signal insufficient explanation of concepts, lack of examples, or minimal contextual integration of specialized terminology.

• Reasoning: Low scores indicate sections may present statements or instructions without cause–effect logic, stepwise explanations, or coherent argumentation.
• Contextual: Low scores show that the content may fail to provide supporting context, such as use cases, examples, or domain-specific references that help interpret instructions practically.

High Variance Components

Variance across sections reveals inconsistency in expertise signals. A high variance in a component like depth means that some sections are highly detailed while others are shallow, leading to uneven perceived authority. Low variance with low scores indicates consistently poor coverage, while high variance with medium or high mean suggests areas of excellence coexisting with weaker sections.

• Interpretation: High variance in depth implies the need for standardization—ensuring all sections maintain a minimum level of comprehensive detail to avoid fragmented authority.

Section-Level Expertise Evaluation

Strongest Sections

Strong sections are those with the highest aggregate expertise scores. They typically combine deep content, precise terminology, structured reasoning, contextual examples, and confident presentation.

·         Strength Indicators:

• High terminology scores suggest domain-specific language is correctly applied.
• Depth scores indicate detailed coverage of the topic.
• High reasoning and contextual scores show logically structured content and meaningful context.

Even the strongest sections may reveal gaps such as missing real-world examples, low assertiveness, or cautious phrasing, indicating potential improvements to maximize perceived authority.

Weakest Sections

Weak sections reveal areas of concern that may undermine overall page expertise. Low scores are often found in content-heavy technical instructions, code snippets, or procedural guidance without contextual explanation.

·         Thresholds:

• Scores below 30 suggest critical deficiencies requiring immediate attention.
• Scores between 30–50 suggest moderate weaknesses that may reduce engagement or authority perception.

·         Interpretation: Weak sections commonly suffer from poor reasoning structure, insufficient contextual grounding, or low confidence in phrasing. Correcting these issues can improve both SEO performance and perceived expertise.

Section-Level Component Analysis

Component-level scores provide a nuanced understanding of strengths and weaknesses within each section:

• Depth: Evaluates completeness and level of detail. Low scores suggest superficial coverage or missing elaboration.
• Terminology: Assesses correct and precise use of domain-specific vocabulary. High scores indicate strong domain alignment.
• Reasoning: Measures logical flow, cause–effect clarity, and instructional coherence. Low reasoning scores point to sections that present steps or facts without clear logic.
• Contextual: Determines integration of examples, references, and relevant context. Low contextual scores reveal a lack of applied understanding.
• Confidence: Represents assertive language and authoritative tone. Low confidence scores often correlate with hedging or overly cautious phrasing.

Actionable recommendations for section improvement typically focus on adding examples, clarifying cause–effect relationships, and strengthening authoritative tone.

Expertise Gaps Analysis

Expertise gaps are recurring deficiencies identified across sections. Common gaps include:

·         Missing Examples: Sections lack real-world scenarios to illustrate points.

·         Weak Reasoning Structure: Logical flow is unclear or steps are not fully explained.

·         Excessive Hedging: Language is overly cautious, reducing perceived authority.

·         Low Expert Assertiveness: Statements lack confidence and certainty.

·         Interpretation and Action: Addressing gaps systematically ensures more consistent authority signals. For example, adding clear examples and stepwise explanations enhances reasoning and contextual scores, while rephrasing cautious statements improves confidence.

Visualization Insights

Visualization modules provide an intuitive, at-a-glance understanding of expertise distribution, section performance, and component consistency. The key plots are:

Component Distribution Plot

This grouped bar chart compares mean component scores across pages. Each component (depth, terminology, reasoning, contextual, confidence) is visualized per page to highlight relative strengths and weaknesses.

• Interpretation: Components significantly below others indicate systemic weaknesses. High consistency across pages in terminology but lower reasoning highlights areas where content may appear authoritative in language but lacks logical flow.
• Actionable Insight: Prioritize improvements in components consistently low across multiple pages to uplift overall authority.

Section Scores Plot

Horizontal bar charts show top and bottom performing sections for each page.

• Interpretation: High-scoring sections indicate best practices, while low-scoring sections indicate urgent areas for enhancement. The gap between the top and bottom sections illustrates inconsistency.
• Actionable Insight: Focus on raising weaker sections closer to top-performing examples through enhanced depth, contextualization, and reasoning.

Component Heatmap

Displays normalized component scores for the top sections, allowing inspection of how different expertise signals vary within high-performing sections.

• Interpretation: Sections with high depth but low reasoning or confidence indicate isolated strengths. Heatmaps reveal patterns where certain components consistently lag even in otherwise strong sections.
• Actionable Insight: Target specific components within high-performing sections to balance expertise signals and maintain uniform quality.

Gap Frequency Chart

Bar chart listing the most common expertise gaps across sections.

• Interpretation: Recurrent gaps identify systemic content deficiencies, such as missing examples or weak reasoning.
• Actionable Insight: Implement content templates or structured guidelines addressing frequent gaps to standardize expertise across the page.

Component Variance Plot

Shows variance for each component across sections, indicating consistency of expertise signals.

• Interpretation: High variance signals uneven coverage; low variance suggests either uniformly high or uniformly low quality. Components with high variance but medium mean scores need targeted normalization to ensure overall authority is not diluted.
• Actionable Insight: Reduce variance by improving weaker sections to match the stronger sections for each component.

Practical Takeaways and Action Recommendations

• Focus improvements on components and sections with low scores or high variance, especially reasoning and contextual aspects.
• Strengthen weak sections by adding detailed examples, clarifying logical steps, and adopting a more assertive tone.
• Use top-performing sections as internal benchmarks to align weaker content.
• Monitor recurring gaps and address them across all sections to maintain consistent expertise signals.
• Leverage visualization outputs for quick assessment and ongoing content quality tracking, ensuring systematic, data-driven content improvement.

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Q&A Section — Result-Based Insights and Actionable Guidance

This section provides practical questions and detailed answers to help interpret the results, derive insights, and take concrete actions to enhance page authority and content effectiveness.

Which sections should be prioritized for improvement and why?

Sections with the lowest overall expertise scores should be the first focus. Scores below 50 indicate critical gaps in depth, reasoning, contextual examples, or confidence. Weak sections often correspond to highly technical instructions, procedural content, or areas with minimal elaboration. Prioritizing these sections ensures that content becomes more authoritative, logically structured, and user-friendly. Addressing these sections first also improves page consistency, reducing variance and strengthening systemic expertise signals.

How can low reasoning and contextual scores be addressed effectively?

Low reasoning scores indicate a lack of logical flow or cause–effect relationships in the content. To improve reasoning, add clear step-by-step explanations, comparisons, and cause–effect narratives. Contextual scores measure the integration of real-world examples, scenarios, and relevant references. Enhancing contextual content involves providing practical applications, case studies, or illustrative examples that align with the topic. Together, improvements in reasoning and context increase the credibility and perceived expertise of the page.

What role do confidence scores play, and how can they be improved?

Confidence scores reflect the assertiveness and authoritative tone of the content. Low confidence scores often result from hedging language or cautious phrasing, which can reduce the perceived expertise even in technically correct content. Improvement involves rephrasing statements to be more assertive, using declarative sentences, and incorporating authoritative expressions. Clear, confident language complements strong reasoning and contextual examples, reinforcing overall page authority.

How can the analysis of strongest sections guide content strategy?

Top-performing sections serve as internal benchmarks for content quality. They demonstrate best practices in depth, terminology, reasoning, contextual integration, and confidence. By analyzing these sections, similar structures, explanatory patterns, and authoritative phrasing can be replicated in weaker areas. This approach ensures consistency, reduces variance, and elevates overall page expertise.

How should expertise gaps be addressed across multiple sections?

Frequent gaps such as missing examples, weak reasoning, or excessive hedging indicate systemic issues. Addressing these requires a structured content enhancement plan:

1. Identify recurring gaps from the gap frequency analysis.
2. Develop content templates or guidelines to standardize logical flow, contextual examples, and confident phrasing.
3. Apply these guidelines across low-scoring sections to ensure uniformity in expertise.

Systematic gap remediation ensures more balanced expertise distribution and strengthens the credibility of the page as a whole.

What insights can be drawn from component variance, and how can actions be prioritized?

High variance in components such as depth or reasoning signals uneven quality across sections. This may confuse readers and weaken overall perceived authority. Actions should be prioritized by focusing on sections where components are both low and inconsistent. Standardizing coverage by enhancing weaker sections to match the stronger sections reduces variance, ensuring each section meets a minimum expertise threshold. Consistent component scores across sections improve page reliability, reader trust, and SEO authority signals.

How can visualizations support ongoing content optimization?

Visualizations translate complex score data into intuitive insights:

• Component Distribution Charts highlight systemic weaknesses across pages.
• Section Scores Plots identify top and bottom-performing sections for targeted improvements.
• Heatmaps reveal component-level patterns within top sections, showing strengths and gaps.
• Gap Frequency Charts expose recurring deficiencies across sections.
• Variance Plots indicate inconsistency and priority areas for alignment.

Regularly monitoring these visualizations helps track improvements over time, ensures content meets expertise standards, and enables data-driven decision-making for ongoing content strategy.

What are the practical benefits of implementing recommendations from this analysis?

Enhancing low-scoring sections and addressing recurring gaps provides multiple benefits:

• Improved semantic authority, leading to stronger E-E-A-T signals.
• Increased reader engagement through clearer explanations, examples, and actionable guidance.
• More consistent content quality across sections, reducing variance and improving trustworthiness.
• Better alignment with SEO and user intent, contributing to higher search rankings and audience satisfaction.
• Efficient prioritization of content updates, ensuring maximum impact with minimal effort.

Implementing these actions systematically creates a content ecosystem that is authoritative, comprehensive, and optimized for both search engines and readers.

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Final Thoughts

The project “Expertise Depth Analyzer — Evaluating Content Authority Through Multi-Signal Semantic Indicators” successfully assessed the depth, authority, and overall expertise of content across multiple pages using a combination of semantic and component-level signals. The analysis provided a comprehensive understanding of how each section contributes to the perceived authority of the page and highlighted areas of strength as well as recurring expertise gaps.

Page-level expertise scores delivered clear benchmarks, with mean and median scores allowing for an immediate understanding of overall content authority. Strong sections exemplified best practices in depth, reasoning, contextual integration, and confident expression, serving as internal reference points for maintaining consistency across content. Weak sections were identified with precise component-level insights, providing targeted areas for enhancement to elevate the overall semantic authority.

The section-level highlights further refined the understanding of content performance, showing how individual components—depth, terminology, reasoning, contextual relevance, and confidence—combine to shape the perceived expertise. Gap analysis illuminated frequent deficiencies such as insufficient examples, weak reasoning structures, or hedging language, allowing for precise content interventions that directly enhance E-E-A-T signals.

Visualization modules translated complex score data into intuitive insights. Component distribution plots revealed systemic patterns across pages, heatmaps illustrated detailed section-level strengths and weaknesses, gap frequency charts highlighted recurring deficiencies, and variance plots emphasized consistency across content. These visualizations supported actionable interpretation, making it easier to identify priority areas and replicate best practices from top-performing sections.

Overall, the project achieved a robust, multi-dimensional evaluation of content authority, providing actionable insights into content quality, semantic depth, and structural consistency. The results empower the optimization of existing pages and inform strategies for creating highly authoritative, contextually rich, and well-reasoned content that aligns with best practices for expertise and trustworthiness in digital content.