Named Entity Recognition (NER) Enhanced Ranking – Extracts And Ranks Pages Based On The Prominence Of Named Entities

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The project, titled “Named Entity Recognition (NER) Enhanced Ranking, Extracts and ranks pages based on the prominence of named entities,” introduces a content-centric evaluation system that assesses webpages through the lens of named entity analysis. Named entities include identifiable real-world concepts such as people, organizations, locations, products, events, and more.

Rather than relying solely on surface-level SEO indicators like keyword frequency or meta tags, this Instead of relying solely on surface-level SEO signals like keyword frequency or meta tags, this system leverages advanced language models to extract meaningful entities from webpage content. These entities are then analyzed for their variety, frequency, and contextual confidence to compute an entity-based score for each page.

The scoring logic reflects a page’s semantic depth and topical richness, making it a robust signal for ranking content based on real informational value. This approach allows for objective, scalable evaluation across pages and websites.

Project Purpose

The primary goal of this project is to help businesses:

• Identify high-value content that demonstrates topical authority through strong usage of meaningful named entities.
• Evaluate and compare webpages based on entity prominence rather than superficial SEO metrics.
• Detect content gaps, where weak entity usage signals opportunities for improvement.
• Visualize content quality through charts showing entity types, density, and diversity on each page.

This entity-focused evaluation provides deeper insight into how thoroughly a page covers key concepts in its domain, enabling better strategic decisions for content optimization, auditing, and competitive benchmarking.

What is Named Entity Recognition (NER)?

Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique that identifies and classifies words or phrases in text into predefined categories. These categories represent real-world objects or abstract concepts, such as names of people, organizations, locations, products, and more.

NER models automatically scan content to highlight critical elements, similar to how a human reader identifies key terms, but at scale and programmatically.

Common Types of Entities Detected by NER

NER models typically recognize a wide range of entities, including:

• PERSON: Names of individuals (e.g., Elon Musk, Marie Curie)
• ORG (Organization): Companies, institutions, or teams (e.g., Google, Microsoft, WHO)
• GPE (Geopolitical Entity): Countries, cities, or regions (e.g., India, New York, EU)
• LOC (Location): Physical locations not covered under GPE (e.g., Himalayas, Pacific Ocean)
• PRODUCT: Consumer or industrial products (e.g., iPhone, Tesla Model 3)

The exact entity set depends on the NER model used. This project employs a state-of-the-art model trained on high-quality, real-world datasets to ensure precise and meaningful entity extraction.

Why Named Entities Matter in Content Evaluation

Named entities represent the core informational elements of a page, revealing what the content is truly about, whether it’s a product review, biography, business profile, or legal article.

Pages rich in relevant entities tend to:

• Demonstrate topical depth and authority
• Align more accurately with user intent
• Be more informationally valuable

By evaluating named entities, content assessment becomes meaningful and scalable, moving beyond shallow metrics like keyword repetition.

Why NER is Ideal for Web Content Analysis

NER is particularly effective for online content because:

• Webpages often include structured or semi-structured text (product names, brands, locations).
• Entities frequently repeat across pages, enabling comparisons and pattern recognition.
• It supports language-independent understanding, since entities remain consistent across languages (e.g., Google, Tesla, COVID-19).

These factors make NER a powerful tool for content audits, topical relevance checks, and ranking strategies in SEO and content marketing.

NER vs Traditional Keyword Extraction

Unlike basic keyword extraction, which often relies on frequency or statistical co-occurrence, NER identifies real-world references and understands context.

Example:

• A keyword tool may flag the word “apple” frequently.
• NER determines if “Apple” refers to a company (ORG) or a fruit (PRODUCT) based on context.

This context-aware analysis makes NER more accurate, especially for long-form or technical content.

Libraries Used

requests

What it is: A widely used Python library for sending HTTP requests.

Why it is used: Fetches raw HTML content from URLs, providing the web pages that will be analyzed for named entities.

BeautifulSoup (from bs4)

What it is: A Python library for parsing HTML and XML documents.

Why it is used: Extracts clean, structured text from HTML tags such as headings, paragraphs, and lists, ensuring the content passed to the NER model is meaningful and free of clutter.

re (Regular Expressions)

What it is: A built-in Python module for pattern matching and text

Why it is used: leans and preprocesses text by removing non-text characters, unwanted line breaks, or HTML artifacts before sending it to the NER model.

collections.Counter and collections.defaultdict

What it is: Built-in Python tools for counting and grouping.

Why it is used:

• Counter: Tracks the frequency of each named entity for frequency-based analysis.
• defaultdict: Efficiently groups entities with metadata (e.g., type, position) while processing multiple text blocks.

numpy

What it is: A core scientific computing library optimized for arrays and numerical operations.

Why it is used: Supports calculations such as averages and ratios during entity ranking, enabling fast and efficient numerical handling.

pandas

What it is: A Python library for structured data analysis (tables or spreadsheets).

Why it is used: Organizes extracted entities and their scores, allowing easy sorting, transformation, and preparation for visualization or export.

matplotlib.pyplot and seaborn

What they are: Python visualization libraries

• matplotlib.pyplot: Provides foundational plotting tools.
• seaborn: Enhances matplotlib with improved aesthetics and convenience.

Why they are used: Visualize the distribution and prominence of named entities across pages, helping clients understand content quality and topical coverage at a glance.

flair

What it is: A powerful natural language processing (NLP) library developed by Zalando Research.

Why it is used: This library provides access to pretrained NER models, including models trained on high-quality datasets like OntoNotes. In this project, flair is used to perform Named Entity Recognition — the core function that extracts real-world entities (e.g., people, organizations, locations) from the web page content.

Sentence (from flair.data): Helps convert raw text into a format that can be processed by the NER model.

SequenceTagger (from flair.models): Loads the pretrained model used for tagging named entities within the text.

Function: extract_text(url)

Function Purpose:

This function is responsible for retrieving and cleaning the main readable content from a given webpage. It prepares the text for Named Entity Recognition (NER) by removing clutter like ads, navigation bars, and code snippets — ensuring only meaningful content like headings and paragraphs is kept.

Fetch the Webpage

response = requests.get(url, timeout=10) response.raise_for_status()

• This line connects to the web page and downloads its content.
• The timeout=10 ensures the request won’t hang for too long.
• If the page cannot be reached or returns an error (e.g., 404 Not Found), the function gracefully handles it by printing an error and returning an empty string.

Parse the HTML

soup = BeautifulSoup(response.text, ‘html.parser’) elements = soup.find_all([‘p’, ‘h1’, ‘h2’, ‘h3’])

·         BeautifulSoup is used to read the HTML and make sense of the structure of the web page.

·         Only key content tags are selected:

• Paragraph text(<p>), Headings(<h1>, <h2>, <h3>)

·         This excludes menus, scripts, and decorative elements that don’t contribute meaningful textual content.

Clean and Filter the Text

text = el.get_text(separator=’ ‘, strip=True) if len(text) >= 30: visible_text.append(text)

• Each selected element is converted into clean, plain text.
• Blocks shorter than 30 characters are skipped to avoid noise such as headings like “Read More” or “Contact Us”.

Return Final Output

return “\n”.join(visible_text)

• The meaningful text blocks are joined into a single string, separated by line breaks.
• This final output is ready to be passed to the NER model for further analysis.

Why This Matters:

• This function ensures that only valuable content reaches the NER model.
• By avoiding noisy elements, it increases the accuracy of entity recognition, making the overall scoring and ranking system more reliable.

Function: preprocess_text(raw_text)

Function Purpose:

This function is designed to clean and normalize the raw text extracted from a webpage before it is sent to the NER model. The goal is to reduce noise and make the text easier for the model to understand, improving the accuracy and consistency of entity recognition.

Step-by-Step Breakdown

Remove URLs

text = re.sub(r’\b(?:https?|www|ftp)\S+\b’, ”, raw_text)

• Removes links like https://example.com or www.website.com.
• URLs rarely contribute useful named entities and may distract the model.

Remove Special Characters

text = re.sub(r'[^A-Za-z0-9\s]’, ‘ ‘, text)

• Strips out symbols, punctuation marks, and special characters.
• This focuses the text on actual words and meaningful sequences.

Remove Extra Spaces

text = re.sub(r’\s+’, ‘ ‘, text)

• Replaces multiple spaces or irregular spacing with a single space.
• Improves text uniformity and model readability.

Trim Whitespace

text = text.strip()

• Removes any leading or trailing white spaces.

Remove Numbers

text = re.sub(r’\d+’, ”, text)

• Numbers (years, phone numbers, etc.) are removed unless relevant to the specific use case.
• This step is helpful in general content understanding where numbers often do not indicate named entities.

Convert to Lowercase

text = text.lower()

• Converts all text to lowercase.
• Helps normalize entity recognition (e.g., recognizing “Google” and “google” as the same).

Why This Matters:

• Preprocessing ensures that only clean, consistent, and structured text is passed to the NER model.
• This helps the model reduce confusion, identify entities more accurately, and avoid being misled by clutter or formatting.
• Without this step, results could include irrelevant or missed entities due to poor formatting or noise in the data.

Function: load_model()

Loads a pre-trained Named Entity Recognition (NER) model using the Flair library.

Why It’s Used:

Provides a ready-to-use model for identifying named entities in text, allowing for fast integration without custom training

Model Section: Flair NER (OntoNotes-Large)

The Named Entity Recognition (NER) functionality in this project is powered by the Flair NER model based on the OntoNotes 5.0 dataset. This model is one of the most comprehensive publicly available NER models and is integrated via the Flair NLP framework.

What Is This Model?

The backbone of this project is a Named Entity Recognition (NER) model called flair/ner-english-ontonotes-large. It is a transformer-based model built on XLM-RoBERTa and trained on the OntoNotes 5.0 dataset, a well-known benchmark in the NLP community for tasks involving linguistic annotations. This model is integrated through the Flair NLP framework and is specialized in identifying a wide range of named entities across diverse types of online text, including webpages, news articles, and social media.

What This Model Does

The model analyzes web content to identify spans of text corresponding to real-world concepts such as people, organizations, places, events, and more. For each recognized entity, it assigns one of 18 predefined categories, allowing the system to understand which concepts are present on a page and which entity types dominate the content.

It operates token by token using the BIO tagging scheme (Beginning, Inside, Outside), which marks whether each token starts an entity (B), continues an entity (I), or lies outside any entity (O). This approach enables the accurate detection of multi-word entities like “New York City” or “International Business Machines.”

Underlying Architecture of the Model

The model is built on XLM-RoBERTa, a large-scale transformer pretrained on multiple languages and known for capturing rich contextual semantics. Text is processed through 24 transformer layers using self-attention mechanisms. The model generates dense vector embeddings of words and uses a linear classifier head to convert these embeddings into named entity labels based on learned patterns.

This architecture allows the model to achieve state-of-the-art performance across multiple NER benchmarks and domains.

Why This Model Was Selected

This model was chosen for its combination of coverage, precision, versatility, and robustness:

• Broad Entity Coverage: Identifies 18 distinct entity categories, enabling deeper and more nuanced content analysis.
• Contextual Precision: Transformer-based architecture ensures entity predictions consider the surrounding context, resolving ambiguities accurately.
• Domain Versatility: Trained on diverse data types such as news, web content, and conversational text, making it suitable for a wide range of website content.
  
• Robustness to Noise: Performs reliably even on informal or noisy text common on modern web pages.

All Entity Classes of this model

The Flair OntoNotes model predicts one of the following 18 entity classes for each detected entity span:

1. PERSON: Names of individuals, e.g., “Marie Curie”, “Elon Musk”. Useful for identifying influencers, authors, or public figures.
2. NORP: Nationalities, religious groups, or political affiliations, e.g., “American”, “Buddhist”, “Democrats”.
3. FAC: Facilities such as buildings, airports, highways, bridges, e.g., “Golden Gate Bridge”, “JFK Airport”.
4. ORG: Companies, institutions, or agencies, e.g., “Google”, “Harvard University”.
5. GPE: Geo-Political Entities, including countries, cities, and states, e.g., “Germany”, “New York”, “California”.
6. LOC: Non-GPE locations such as mountain ranges, bodies of water, or regions, e.g., “Himalayas”, “Pacific Ocean”.
7. PRODUCT: Consumer or commercial products, e.g., “iPhone”, “Tesla Model 3”, “Microsoft Word”.
8. EVENT: Named events like wars, festivals, or conferences, e.g., “World War II”, “CES 2024”.
9. WORK_OF_ART: Artistic creations including books, songs, movies, and paintings, e.g., “Inception”, “Mona Lisa”, “Hamlet”.
10. LAW: Legal documents or named laws, e.g., “GDPR”, “Civil Rights Act”.
11. LANGUAGE: Human languages, e.g., “English”, “Mandarin”, “Spanish”.
12. DATE: Specific dates or date references, e.g., “January 1st”, “2025”, “last Monday”.
13. TIME: Time expressions, e.g., “3 PM”, “midnight”, “an hour later”.
14. PERCENT: Percentages, e.g., “20%”, “half of the population”.
15. MONEY: Monetary values, e.g., “$100”, “two million euros”.
16. QUANTITY: Measurements and amounts, e.g., “3 kilometers”, “ten liters”.
17. ORDINAL: Ordered numbers, e.g., “first”, “third”, “10th place”.
18. CARDINAL: Non-ordered numbers, e.g., “three”, “100”, “forty-two”.

Strengths of This Entity Set

This extensive set of entity classes enables multidimensional content profiling. For example, a page with high mentions of ORG, PRODUCT, and GPE entities may be commercial or geopolitical in nature, while one rich in PERSON and EVENT entities may reflect news or biographical content. This granularity is essential for ranking and evaluating pages based on topical authority, semantic richness, and overall content quality.

Why NER Matters Beyond Traditional SEO

One of the most important shifts in modern content evaluation is the move away from surface-level keyword matching toward understanding the meaning behind text. Traditional SEO metrics like keyword frequency or meta tag optimization are limited because they treat terms as isolated tokens rather than semantically meaningful concepts.

Named Entity Recognition elevates this by identifying real-world entities, people, organizations, locations, dates, and more, and mapping them to contextual categories that machines and search systems can interpret as structured data. This capability is a key reason why advanced search engines, recommendation systems, and AI assistants are increasingly incorporating entity understanding into ranking algorithms. Many search engines now consider entities a more reliable signal of topic authority than keyword occurrence alone.

Beyond SEO, NER plays a growing role in knowledge graph construction, which is the backbone of many intelligent features like related content suggestions, semantic search, and entity‑aware personalization. When a page mentions “Tesla,” not only is it recognized as a product or organization, but it also connects to a larger graph of related entities, such as its CEO, models, industry news, or market trends, enabling richer search and discovery experiences for users.

Function: extract_named_entities()

This function is the core NER extraction module of the project. It takes in a block of cleaned text and uses the Flair OntoNotes NER model to identify and return relevant named entities with associated metadata such as entity type and confidence score.

sentence = Sentence(text)

• What it does: Converts raw text into a format understood by the Flair NER model.
• Why it matters: Flair requires text to be wrapped inside a Sentence object to process it for entity recognition.

tagger.predict(sentence)

• What it does: Runs the pre-trained NER model on the input sentence and attaches entity predictions to it.
• Why it matters: This is the core prediction step where named entities are extracted by the model.

for entity in sentence.get_spans(‘ner’)

• What it does: Iterates through all identified named entity spans in the sentence.
• Why it matters: Flair groups recognized entities into spans like names, organizations, locations, etc. This loop retrieves each of them for processing.

label = entity.get_label(‘ner’).value

• What it does: Retrieves the predicted label (entity type) such as PERSON, ORG, PRODUCT, etc.
• Why it matters: Helps understand what kind of entity was detected—this is the semantic category used for scoring and visualization.

score = entity.get_label(‘ner’).score

• What it does: Gets the model’s confidence in the prediction (between 0 and 1).
• Why it matters: Allows filtering out low-confidence predictions to improve reliability.

if min_score_threshold and score < min_score_threshold: continue

• What it does: Skips entities that fall below the minimum confidence threshold.
• Why it matters: Filters out uncertain predictions, ensuring only reliable entities are used.

if exclude_keywords and any(re.search(pat, word.lower()) for pat in exclude_keywords): continue

• What it does: Skips entities that match any unwanted keyword or pattern (like “test”, “lorem”, etc.).
• Why it matters: Helps remove noisy or irrelevant terms that don’t add value to the ranking.

if word.lower() not in seen_words:

• What it does: Ensures that each entity is counted only once (case-insensitive).
• Why it matters: Prevents over-representation of repeated terms in the same text block.

entities.append({…})

• What it does: Stores valid entity data (word, label, confidence) into a structured list.
• Why it matters: This final list is used for scoring, ranking, and visualizations.

Understanding the Named Entity Output

Each dictionary inside the output list represents a named entity detected from the cleaned text using the Flair NER model. The components are:

1.    word

• The exact term or phrase from the text identified as an entity.
• Example: “forbes agency council”, “today”, “three”

2.    entity_group

o The type or class of the entity, such as:

• ORG: Organizations
• DATE: Temporal references (dates, time expressions)
• CARDINAL: Numbers (counts, quantities)

o These help categorize what the term represents in context.

3.    score

• Confidence score (between 0 and 1) showing how certain the model is about the classification.
• Higher values (closer to 1) indicate greater certainty.
• Example: “forbes agency council” has a score of 0.9995, showing very high confidence.

Function: create_entity_profile

This function is responsible for creating a summary profile of named entities extracted from a web page. It analyzes the list of named entities and aggregates them based on their entity type (like ORG, PERSON, DATE, etc.). The output is a dictionary that shows how many times each entity type appears, helping to understand what kinds of named entities dominate the content.

This is useful for:

• Highlighting which entity categories are prominent on a page.
• Comparing entity distributions across different pages or websites.
• Providing high-level insights for visualizations or content audits.

Explanation

entity_count = Counter()

• Initializes a Counter object from the collections module.
• This will store counts of how many times each entity type appears.

for entity in ner_results:

• Iterates over each named entity returned by the NER model.

entity_group = entity[‘entity_group’]

• Extracts the entity type label (e.g., ‘ORG’, ‘PERSON’) from the result.

entity_count[entity_group] += 1

• Increments the count for the corresponding entity type.
• For example, if ‘ORG’ is seen multiple times, it keeps a running total.

return dict(entity_count)

• Converts the Counter object into a standard Python dictionary for easier use later (e.g., in plots or scoring logic).

Interpreting the Output

Each key in the dictionary is an entity type, and the value is the number of times that type appeared in the webpage’s text. For instance:

‘ORG’: 7

There are 7 occurrences of entities classified as organizations.

‘GPE’: 27

27 geopolitical entities (such as countries, cities, or states) were detected — this is a strong indicator that location-based content dominates the page.

‘CARDINAL’: 3

Three numerical expressions (like counts, rankings, etc.) were found.

‘WORK_OF_ART’: 1

There’s one mention of an artistic title or creative work.

Practical Significance

This profile helps answer questions like:

• What kind of content does this page focus on?
• Is the content more people-centric (PERSON) or organization-centric (ORG)?
• Does the content include many locations (GPE) or dates (DATE)?
• Are there creative assets or references (e.g., WORK_OF_ART)?

Such insights are valuable for SEO strategists, content auditors, and analysts evaluating whether a webpage aligns with topical authority or target audience relevance.

Function: calculate_entity_score

This function evaluates a web page based on its named entities using three key components:

• Diversity – Measures how many different types of entities appear (e.g., ORG, PERSON, GPE).
• Density – Measures how frequently entities appear in proportion to total word count.
• Confidence – Measures the average prediction score of all detected entities.

Each of these components contributes to a final score that reflects how entity-rich and semantically structured the page is — crucial for evaluating topical authority and content depth in SEO.

Explanations:

Entity Weighting

ENTITY_WEIGHTS = {…}

Each entity type is given a predefined weight based on its assumed semantic value. For example:

• ‘ORG’ and ‘PERSON’ have the highest weight (1.2) — indicating importance for authoritative content.
• ‘CARDINAL’ and ‘ORDINAL’ have the lowest (0.03, 0.05) — often add noise and are less semantically useful.

Weighted Diversity Score

unique_types = set(ent[‘entity_group’] for ent in entities)

• This line identifies how many distinct entity types are present.

diversity_score = sum(ENTITY_WEIGHTS.get(t, 0.0) for t in unique_types) max_possible_diversity = sum(sorted(ENTITY_WEIGHTS.values(), reverse=True)[:len(unique_types)]) weighted_diversity_score = diversity_score / max_possible_diversity

• It normalizes the diversity score by comparing actual diversity against the ideal max diversity if the top-N most important types were all present.

Density Score

weighted_entity_count = sum(ENTITY_WEIGHTS.get(ent[‘entity_group’], 0) for ent in entities) density_score = weighted_entity_count / (total_word_count / 1000)

• Computes how many weighted entities appear per 1000 words — a length-adjusted frequency.

final_density_score = np.log1p(density_score) / np.log1p(MAX_REASONABLE_DENSITY)

• Applies log-scaling to prevent overly frequent entities from dominating the score.
• Caps at an upper bound of 50 entities per 1000 words to avoid skew.

Confidence Score

avg_confidence = sum(ent[‘score’] for ent in entities) / len(entities)

• Averages the prediction confidence values of the NER model for all entities, indicating model certainty.

Final Combined Score

final_score = (0.4 * weighted_diversity_score) + (0.4 * final_density_score) + (0.2 * avg_confidence)

• The three components are weighted:
  • Diversity (40%)
  • Density (40%)
  • Confidence (20%)

This balanced weighting ensures that the final score favors rich, frequent, and confidently identified entities.

Interpreting the Output

The goal of this code is to compute a final entity score that reflects the semantic richness of a web page’s content based on named entities.

Explanation:

total_words = len(cleaned_text.split())

• Counts the total number of words in the page content.
• This count is used to normalize entity frequency per 1000 words, helping adjust for document length.

score = calculate_entity_score(ner_results, total_words)

• Calls the previously defined calculate_entity_score function.
• Uses the list of named entities (ner_results) and the word count to generate: Entity Diversity Score, Entity Density Score, Confidence Score, Final Composite Score.

Output Explanation:

1.    Diversity (0.764)

• Indicates moderate-to-high variety in the types of entities used.
• A score of 0.764 means that the content includes multiple important types (e.g., ORG, PERSON, GPE), but does not cover the full spectrum of top-weighted entities.

2.    Density (0.65011)

• Reflects the number of weighted entities per 1000 words.
• A 0.65 score here means entities are well distributed through the content, suggesting good semantic saturation without redundancy.

3.    Confidence (0.94981)

• Very high average confidence from the NER model in tagging — nearly 95% average probability.
• This indicates the entities were clearly and correctly identified by the model.

4.    Final Score (0.75561)

• This weighted score combines all three factors, showing an overall strong entity profile for the page.
• A score above 0.75 generally implies the content is semantically rich, diverse, and confidently tagged — important for SEO and topical authority evaluation.

Function: visualize_entity_distribution

The visualize_entity_distribution function generates a bar chart showing the distribution of high-importance named entity types within the content of a specific URL. It filters out less significant types (based on a weight threshold) and displays only the most meaningful entity categories (like ORG, PERSON, GPE, etc.). Each bar reflects the frequency of a specific entity type, helping visualize how dominant or diverse the high-priority entities are in the content.

Explanation:

top_entities = {k for k, v in ENTITY_WEIGHTS.items() if v >= 0.8}

• Defines which entity types are considered important for visualization. Only entities with a weight ≥ 0.8 (e.g., ORG, PERSON, GPE, PRODUCT, etc.) are selected.

filtered_profile = {k: v for k, v in profile.items() if k in top_entities}

• Filters the input profile to retain only those entity types considered important. This ensures the chart remains focused on entities that meaningfully contribute to ranking.

if not filtered_profile: return

• If the filtered profile is empty (i.e., no high-weight entity types present), the function skips visualization for that URL.

df = pd.DataFrame({…}).sort_values(by=”Count”, ascending=False)

• Converts the filtered profile into a DataFrame for plotting, sorting the entity types by descending count for better readability in the chart.

sns.barplot(…)

• Generates the actual bar chart using Seaborn, plotting each high-weight entity type on the x-axis and their corresponding counts on the y-axis. Color is assigned by entity type.

plt.xticks(rotation=45)

• Rotates the x-axis labels for better legibility, especially when there are many types.

plt.title(…) and plt.tight_layout()

• Adds a title and ensures clean layout formatting without overlaps.

This visualization helps content analysts or SEO strategists quickly identify which entity types dominate a page, and whether the distribution aligns with the target domain — e.g., content dominated by ORG, PERSON, and GPE might indicate strong authority-related information.

Explanation of the Generated Visualization:

The function generates a bar chart titled “Entity Type Distribution”. On the x-axis, it shows the filtered entity types:

GPE, ORG, PERSON, EVENT, WORK_OF_ART.

On the y-axis, it displays their corresponding frequencies:

GPE: 27 occurrences — the tallest bar, clearly dominating the chart.

ORG: 7 occurrences — second highest bar, indicating a strong presence of organization-related entities.

PERSON: 1 occurrence

EVENT: 1 occurrence

WORK_OF_ART: 1 occurrence

The last three are shorter bars of equal height (count = 1), showing minimal presence.

This visualization quickly communicates that the geopolitical entities (GPE) are heavily represented, followed by ORG, while other high-weight categories are present in much smaller numbers. It helps assess if the content is overly focused on location-based mentions, potentially revealing a skew that may or may not align with content goals (e.g., global authority vs. local focus). 

Function: visualize_score_breakdown()

The function visualize_score_breakdown is designed to create a bar plot that visually represents the breakdown of the entity score components for a particular URL. These components include diversity, density, and confidence scores. The scores are displayed as separate stacked bars, allowing a clear visual understanding of how each component contributes to the overall score for the URL. The function takes a list of score breakdowns and plots them using a bar chart.

Explanation:

DataFrame creation for plotting

df = pd.DataFrame({“Component”: [“Diversity”, “Density”, “Confidence”], “Score”: [score[“Diversity”], score[“Density”], score[“Confidence”]] })

• This line creates a Pandas DataFrame that holds the names of the score components (Diversity, Density, and Confidence) as well as their corresponding values. The score dictionary is passed to extract each component’s value.

Plotting the bar chart

sns.barplot(data=df, x=”Component”, y=”Score”, hue=df[‘Component’], legend=False)

• This line uses Seaborn’s barplot to plot the bar chart. The x axis represents the components (Diversity, Density, Confidence), and the y axis represents the corresponding scores. The hue parameter is used to differentiate the bars based on their component type.

Set plot limits and title

plt.ylim(0, 1)

• plt.title(f”Score Breakdown”) The y-axis limit is set to be between 0 and 1 to ensure the scores fall within a standardized range. The plot title “Score Breakdown” is added to describe the chart.

Tight layout and display

plt.tight_layout() plt.show()

• plt.tight_layout() adjusts the layout to ensure that the plot components do not overlap. plt.show() is used to display the plot.

Visualization Output:

The bar chart produced by this function contains three vertical bars, each corresponding to one of the score components:

• Diversity — the first bar will show a height of 0.764, representing the weighted variety of entity types detected in the content.
• Density — the second bar will rise to 0.65011, indicating the relative frequency of named entities across the text, normalized per 1000 words.
• Confidence — the third bar will nearly reach the top, at 0.94981, representing the model’s average certainty across all extracted entities.

All bars are plotted on a common vertical scale from 0 to 1, allowing for easy comparison across the three dimensions. The bars are color-coded by component and clearly labeled on the x-axis. The chart’s title is “Score Breakdown”, and there’s no legend since the hue coloring is self-explanatory.

This visualization gives an at-a-glance summary of how balanced and strong the named entity profile is for the URL — combining entity variety, frequency, and reliability.

Result Discussion — Multi-URL Named Entity Analysis

In this section, the entity-driven analysis was extended from a single URL to a list of URLs, enabling a comparative evaluation of content quality and topical authority across multiple web pages. Each URL underwent named entity recognition (NER), followed by profiling, scoring, and visualization. This multi-URL approach provides deeper insight into how consistently a website incorporates meaningful, high-confidence named entities across its pages, a critical factor in both topical relevance and semantic richness.

Future‑Ready SEO Signals

Entities, especially those tied to widely recognized knowledge sources, are increasingly used in semantic indexing and ranking algorithms. Search engines are moving toward entity‑based indexing systems where pages are evaluated on the concepts they represent rather than the words they contain. This means that content articulated around well‑linked entities with clear context has a better chance of ranking for related queries, even if the exact search terms aren’t present.

Entity Diversity Across Pages

Each page was evaluated for its diversity of entity types, placing greater value on entities such as organizations (ORG), persons (PERSON), and locations (GPE). Pages with a wider range of these higher-weight categories reflect a richer informational context. Diversity, in this sense, isn’t merely about quantity but about variety and relevance of named entities, which often correlates with broader topic coverage and higher semantic specificity.

Pages with limited entity variety tend to be more narrow in scope, potentially missing opportunities to signal domain authority or topical depth to both users and search engines.

Entity Density as a Signal of Informational Density

Another dimension evaluated was entity density — the number of weighted entities normalized over 1,000 words. This metric helps distinguish between content that is densely packed with meaningful references versus pages that are sparse or overly generic. High-quality pages typically exhibit a balance: they mention enough prominent entities to anchor the content in reality without being overly repetitive or keyword-stuffed.

Entity density also indirectly reflects the information-to-noise ratio — valuable when assessing whether a page offers concrete, referential knowledge or leans heavily on filler content.

ConConfidence as a Signal of Model Certainty

The confidence score, derived from the NER model’s certainty for each entity extraction, is a critical measure of content clarity and precision. High confidence typically corresponds to well-structured, coherent text, where entities are clearly expressed and unambiguous. Conversely, pages with inconsistent sentence structure, ambiguous phrasing, or excessive jargon tend to produce lower confidence scores, reducing the reliability of entity-based evaluation.

Visualization Interpretation

Two types of visualizations help interpret the results:

• Score Breakdown Charts: These display diversity, density, and confidence for each URL side by side. They make it easy to spot imbalances, such as high entity density but low diversity, which could indicate over-optimization or narrow content focus.
• Entity Type Distribution Charts: These show which entity types dominate a page. For example, content focused heavily on GPE (locations) may reflect geographic targeting, while a page rich in ORG and PERSON entities could indicate brand authority, expert contributions, or case studies.

Content Consistency and Strategic Opportunities

Comparing multiple URLs uncovers patterns: some pages consistently perform well across all metrics, while others may lack entity richness or confidence. These insights can guide content audits and SEO strategy improvements, such as:

• Enhancing underperforming pages by including more precise references (e.g., organizations, experts, products).
• Structuring content to improve readability and model confidence.
• Identifying content clusters with strong entity presence to reinforce authority signals.

What This Project Reveals About Your Website Content

This project evaluates your content using Named Entity Recognition (NER), which identifies key real-world references such as organizations, locations, people, products, and dates. By scoring pages on entity diversity, density, and confidence, you gain an objective measure of semantic richness, clarity, and topical authority.

In simpler terms, it answers: Are we discussing the right topics, clearly and credibly, and frequently enough to matter?

Understanding Confidence Scores

The confidence score reflects the NER model’s certainty that a text contains valid named entities. High confidence typically means your content is:

• Grammatically structured
• Semantically clear
• Free from ambiguity or noise

If a page scores low, consider:

• Rewriting unclear or fragmented sentences
• Reducing jargon or filler content
• Breaking long, complex sentences into simpler structures

Improving confidence not only helps NLP models interpret content accurately but also enhances user comprehension and trust.

Practical Use of Entity Type Distribution Charts

These charts show which entity types dominate each page. For example, a page may heavily reference GPE (locations) or ORG (organizations). Use this information to:

• Align content with search intent: Ensure pages targeting brand-related queries include organizations and product names.
• Balance topic coverage: Avoid overemphasis on a single entity type, which may indicate a narrow topical focus.
• Support internal linking: Pages rich in ORG and PRODUCT references can serve as authoritative anchors in content clusters.

Next Steps After Reviewing Scores and Entity Breakdown

Conduct a Content Audit 

Start by auditing pages that received low or moderate scores in diversity, density, or confidence. Evaluate whether each page:

• References relevant brands, people, locations, or products
• Uses unambiguous language
• Aligns with a focused topic that matches your SEO objectives

This audit helps identify content that is too shallow, vague, or disconnected from authoritative entities in your niche.

Address Low Confidence in Entity Extraction

Low confidence scores often stem from:

• Poor grammar, overly complex sentences, or fragmented structure
• Excessive filler, boilerplate phrasing, or vague writing
• Ambiguity in sentence roles makes it harder for models to detect entities

To improve confidence:

• Simplify sentence structures and enhance readability
• Replace vague terms with specific examples or references
• Use consistent capitalization and punctuation

These adjustments benefit both NER model accuracy and human comprehension, improving clarity, trust, and engagement.

Optimize High-Scoring Pages

Pages with high entity diversity, density, and confidence are prime candidates for:

• Featured snippets or link-building campaigns: their entity-rich content makes them suitable for outreach
• Content repurposing: converting pages into PDFs, videos, or case studies while preserving entity structure
• Anchor content in clusters: using them as authoritative sources to link other content

Maintaining and expanding these high-performing assets supports long-term SEO growth and topical authority.

Final Thoughts

This analysis provides actionable insights into how well your content aligns with both search engine requirements and audience expectations. By evaluating entity diversity, density, and confidence, you gain a clear understanding of your content’s relevance, depth, and credibility.

The scores and visual breakdowns highlight areas of strength while also identifying opportunities for improvement, such as enriching the diversity of referenced entities, increasing entity density, and enhancing clarity and confidence in the text. Entity distribution charts reveal how effectively your content represents key topics, supporting overall content optimization and internal linking strategies.

Moving forward, this data-driven approach should guide your content refinement efforts, helping you stay ahead of SEO trends, strengthen topical authority, and drive more meaningful engagement with your audience. Regular analysis and iterative improvements will ensure that your pages consistently meet the evolving standards of relevance and authority, enabling sustainable growth and stronger positioning in search results.