Bag of Words Hyper-Intelligence SEO For Keyword Targeting

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IIn today’s highly competitive SEO landscape, advanced analytical methodologies play a critical role in sharpening keyword targeting strategies. One such powerful approach is the Bag of Words (BoW) model, a foundational concept in natural language processing (NLP) that converts text into a structured, measurable format. By breaking content into individual words or tokens and ignoring grammar and word order, BoW enables precise evaluation of keyword frequency and relevance.

When applied strategically to SEO, the Bag of Words model reveals actionable insights into keyword distribution, content gaps, and competitive positioning. These insights empower businesses to refine content strategies, align with user intent, and improve search engine rankings with data-driven accuracy.

Bag of Words and SEO Advanced Analysis Techniques for Improved Keyword Targeting

Objective and Purpose of the Project

The primary objective of this project is to deliver a comprehensive analysis of website content by identifying and emphasizing high-impact keywords. This analysis is designed to assist website owners and digital marketers in optimizing content for improved search engine performance, increased organic traffic, and stronger overall SEO results.

The core purpose of this initiative is to examine textual website data and determine which keywords appear most frequently and carry the greatest SEO significance. Through ThatWare’s advanced analytical tool, website owners gain clarity on how effectively their content aligns with targeted SEO goals. These insights enable informed decisions around content refinement, keyword prioritization, and strategic optimization, ultimately enhancing search visibility and attracting highly relevant traffic.

Bag of Words (BoW) Concept

Understanding the Basics

The Bag of Words (BoW) model is a fundamental NLP technique widely used in text analysis and machine learning. It simplifies textual data by decomposing it into individual words or tokens and focusing solely on their frequency of occurrence. Unlike linguistic models that account for sentence structure and word order, BoW emphasizes quantitative representation.

This transformation of text into numerical data makes it possible to analyze, compare, and interpret content at scale, an essential capability for modern SEO strategies.

Key Features of the Bag of Words Model

Tokenization

Tokenization is the initial step in the BoW process, where textual content is divided into individual words or meaningful phrases. This breakdown enables structured analysis and prepares the data for frequency evaluation.

Word Frequency Analysis

After tokenization, the model calculates how often each word appears within the content. These frequency metrics help identify dominant terms that may influence SEO performance and search relevance.

Vector Representation

The final step involves converting the frequency data into numerical vectors. Each document is represented as a vector of word counts, allowing for comparison, pattern recognition, and deeper analytical insights.

Analyzing a Website for SEO

Identifying Key Content Areas

Effective SEO analysis begins with pinpointing high-impact content sections. Priority should be given to core areas such as blog articles, service pages, product descriptions, and landing pages. These elements play a pivotal role in search visibility and should be reviewed for keyword relevance, density, and consistency with the overall SEO strategy.

Conducting Keyword Frequency Analysis

Using tools and techniques like the Bag of Words (BoW) model, perform a keyword frequency analysis on the website’s content. This process highlights which terms dominate the content and whether they align with intended SEO objectives. Keyword frequency analysis provides a clear picture of content focus and optimization opportunities.

Evaluating Keyword Alignment

Once significant keywords are identified, it is essential to evaluate how well they align with search intent and target audience behavior. High-value keywords should be strategically placed within titles, headers, meta descriptions, and body content. Proper distribution ensures maximum visibility while maintaining readability and relevance.

Optimizing for Improved SEO Performance

Insights gained from BoW analysis should inform ongoing content optimization efforts. This includes strengthening keyword usage, filling content gaps, refining keyword density, and enhancing topical relevance. Regularly monitor and update your content to keep it aligned with evolving SEO trends and maintain strong search engine rankings.

For example, if a cooking website consistently highlights terms like “recipe,” “cook,” and “ingredients,” it demonstrates effective keyword targeting within its niche. Conversely, if such keywords are sparse, the website may benefit from expanding recipe-focused content to better engage users and attract relevant search traffic.

Enhancing Bag of Words Analysis with Advanced SEO Techniques

While the Bag of Words (BoW) model provides a strong foundation for keyword analysis, its effectiveness increases significantly when combined with advanced preprocessing and optimization techniques. To extract meaningful SEO insights, raw website text must first be cleaned and structured appropriately. This includes removing HTML tags, scripts, navigation elements, and irrelevant boilerplate content that can distort keyword frequency analysis. By focusing solely on meaningful textual content, SEO professionals can ensure that the results reflect genuine topical relevance rather than technical noise.

Text Preprocessing for Accurate Keyword Insights

Before applying the BoW model, preprocessing steps such as lowercasing text, removing punctuation, and eliminating stop words (e.g., “and,” “the,” “is”) are essential. These common terms carry little SEO value and can inflate frequency counts without contributing to ranking potential. Stemming or lemmatization may also be applied to consolidate word variations, ensuring that terms like “optimize,” “optimizing,” and “optimization” are treated as a single semantic entity. This process improves accuracy and enables clearer identification of dominant keyword themes.

Leveraging N-Grams for Contextual SEO Analysis

A traditional Bag of Words approach focuses on individual words, which can sometimes limit contextual understanding. Introducing n-grams, combinations of two or more consecutive words, enhances keyword targeting by capturing meaningful phrases such as “search engine optimization,” “machine learning SEO,” or “keyword frequency analysis.” These phrase-level insights are particularly valuable for long-tail keyword optimization, voice search readiness, and intent-driven content strategies.

Integrating BoW with Competitive Content Analysis

Beyond internal content optimization, the BoW model can be applied to competitor websites to uncover strategic keyword gaps. By comparing keyword frequency vectors across multiple domains, SEO professionals can identify underutilized terms, emerging topics, and content opportunities. This competitive benchmarking approach supports smarter editorial planning and helps businesses create content that is both relevant and differentiated within their niche.

From Frequency to Importance: Moving Beyond Raw Counts

While BoW emphasizes frequency, not all frequently used keywords carry equal SEO value. Pairing BoW with weighting techniques such as TF-IDF (Term Frequency–Inverse Document Frequency) allows marketers to assess keyword importance relative to competing content. This hybrid approach ensures that optimization efforts focus on keywords that are not only common but also uniquely valuable for ranking.

Strategic SEO Benefits of Automated BoW Implementation

Automating Bag of Words analysis using Python libraries such as BeautifulSoup, CountVectorizer, and pandas enables scalable, repeatable SEO audits. Website owners can routinely monitor keyword trends, evaluate content performance, and adjust strategies in response to algorithm updates or changing search behavior. This data-driven methodology supports continuous optimization, ensuring content remains aligned with both search engine requirements and user intent.

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# Import Necessary libraries.

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

[ ]

url= ‘https://thatware.co/’

# Send an HTTP request to the website

response = requests.get(url)

# Parse the website’s HTML content

soup = BeautifulSoup(response.content, ‘html.parser’)

soup

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<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19281″ id=”menu-item-19281″><a href=”https://thatware.co/case-studies/“><span>SEO CASE STUDIES</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19443″ id=”menu-item-19443″><a href=”https://thatware.co/ai-implementations-seo/“><span>AI CASE STUDIES</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19332″ id=”menu-item-19332″><a href=”https://thatware.co/ai-based-seo-blueprint/“><span>AI SEO BLUEPRINT</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-22251″ id=”menu-item-22251″><a href=”https://www.youtube.com/watch?v=yE6bx2W3GU8“><span>AI-SEO Video</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-22130″ id=”menu-item-22130″><a href=”https://thatware.co/reseller-partnership/“><span>Become Our Reseller</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-22249″ id=”menu-item-22249″><a href=”https://thatware.co/wp-content/uploads/2022/04/ThatWare-Corporate-Profile.pdf“><span>Our Corporate Deck</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-22435″ id=”menu-item-22435″><a href=”https://thatware.co/seo-faq/“><span>SEO FAQ’s for Clients</span></a></li>

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<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-18587 normal-menu” id=”menu-item-18587″><a href=”https://thatware.co/blogs/“><span>BLOGS</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-18352 normal-menu” id=”menu-item-18352″><a href=”https://thatware.co/contact-us/”><span>CONTACT</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-has-children menu-parent-item menu-item-23856 normal-menu” id=”menu-item-23856″><a href=”#”><span>Pricing</span></a><i class=”fa sub-mark fa-angle-down” tabindex=”0″></i>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-21815″ id=”menu-item-21815″><a href=”https://thatware.co/360-seo-package/“><span>360 Degree SEO Package</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-post menu-item-23859″ id=”menu-item-23859″><a href=”https://thatware.co/enterprise-seo-pricing/“><span>Enterprise SEO Pricing</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-22137″ id=”menu-item-22137″><a href=”https://thatware.co/outsource-seo-services/“><span>Outsource SEO</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-post menu-item-23858″ id=”menu-item-23858″><a href=”https://thatware.co/enterprise-vs-360-degree-seo-packages/“><span>Difference Between Enterprise and 360 Degree SEO Packages</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-post menu-item-23860″ id=”menu-item-23860″><a href=”https://thatware.co/offpage-seo-pricing/“><span>Off-Page SEO Pricing Option</span></a></li>

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</header>

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<ul>

<li><h6>173,897,521</h6><p>$ Revenue<br/>Generated via SEO</p></li>

<li><h6>8,898,140</h6><p>Qualified Leads<br/>Generated</p></li>

</ul>

</div>

<!–<h5>AWARDED TOP RATED<br> PROFESSIONAL SEO<br> AGENCY BY<br>

<span class=”item-1″>CLUTCH</span> <span class=”item-2″>MANIFEST</span> <span class=”item-3″>SEM FIRMS</span></h5>–>

<!–<h5>AWARDED TOP RATED PROFESSIONAL SEO AGENCY BY <span>CLUTCH</span>, <span>MANIFEST</span>, <span>SEM FIRMS</span></h5>–>

<!–<h5>Future-Proof SEO: Amplify Visibility through <span>NLP</span>, <span>Semantics</span>, <span>Large Language Models (LLM)</span></h5>–>

<h5>Step into the Future of SEO: Amplify Visibility via <span>NLP</span>, <span>Semantics</span>, <span>Large Language Models (LLM)</span></h5>

</div>

<!–<p class=’seo-ts’>SEO</p>

<p class=’ds-ts’>DATA <br>SCIENCE</p>

<p class=’ml-ts’>MACHINE <br>LANGUAGE</p>–>

</div>

<h5>Pioneer the Fusion of SEO with Artificial Intelligence and Data Science – a World-First Innovation!</h5>

</div>

<h6>Revolutionary SEO: Infusing AI for Unmatched Results – Your Professional SEO Partner!</h6>

</div>

</div>

<h6>Recognized and Awarded Worldwide by 200+ Elite Media Houses, Including:

<li class=”heart”>Forbes Select 200</li>

<li class=”heart”>Clutch</li>

<li class=”heart”>BrightonSEO</li>

<li class=”heart”>GoodFirms</li>

<li class=”heart”>Entrepreneur</li>

<li class=”heart”>Manifest</li>

<li class=”heart”>STEVIE’S</li>

<li class=”heart”>Inventiva</li>

<!–<li class=”heart”>ECONOMIC TIMES</li>–>

</ul>

</h6></div>

</div>

<h2>GET A CUSTOMIZED SEO AUDIT & DIGITAL MARKETING STRATEGY FOR YOUR BUSINESS. START BY SHARING YOUR DETAILS BELOW!</h2>

</div>

</p>

</div><div aria-hidden=”true” class=”wpcf7-response-output”></div>

</form>

</div>

<h5>IF YOU WOULD LIKE TO DISCUSS YOUR SEO CHALLENGES, CONTACT US TODAY BY CLICKING THE LINK BELOW</h5>

<div class=”banner-btn”><a class=”shutter-btn” href=”https://thatware.co/contact-us”>CONTACT US</a></div>

</div>

<h2>AI SEO UNLOCKS THE GOOGLE MAZE</h2>

<p>8 years ago, we embarked on a journey to unravel the intricacies of the Google algorithm—a cryptic enigma begging to be deciphered. Consider it akin to unlocking a closely guarded secret, comparable only to the recipe of Coca Cola or the security measures surrounding the Crown Jewels of London.</p>

<p>To traverse the Google maze, we decided to rewrite the rules and carve our own path. Our strategy? Develop proprietary AI algorithms to adeptly monitor and navigate the evolving landscape of the Google algorithm. To date, we’ve pioneered an impressive portfolio of 753+ unique AI SEO algorithms, elevating the effectiveness and efficiency of our work.</p>

<p>While SEO teams globally have traditionally relied on three key strategies—on-site SEO optimization, backlink building, and content creation and optimization—we at Thatware AI SEO have rewritten the playbook.</p>

<p>Picture this scenario: Your company aspires to secure a coveted spot on page 1 for a strategic keyword. Like clockwork, you scrutinize competitors already occupying that space, pondering the age-old question: “How do I surpass my competitors?”</p>

<p>While conventional SEO companies diligently apply the core elements of on-site optimization, backlink building, and content creation, they often miss a crucial ingredient—Intelligent Guidance.</p>

<p>Here’s our differentiator: Our AI SEO algorithms generate unparalleled “intelligent guidance” unavailable to any other SEO agency globally, revolutionizing the game.</p>

<p>Here’s how it unfolds:</p>

<p>1. Our AI algorithms meticulously analyze your competitors’ sites.</p>

<p>2. They scrutinize your website.</p>

<p>3. They delve into the depths of the Google algorithm itself (yes, we use our AI to monitor Google’s AI).</p>

<p>4. Our AI algorithms provide precise, surgical instructions for implementing the essential aspects of SEO (on-site, backlinks, and content).</p>

<p>5. In essence, our AI SEO algorithms dictate EXACTLY WHAT TO DO to establish your site as the most authoritative for a given keyword, ensuring swift ascent to page 1.</p>

<p>A staggering 95% retention rate across 7400+ clients serves as irrefutable evidence of our success.</p>

</div>

<ul>

<li>

<h6>FASTER RESULTS</h6>

<p>Utilizing 753 proprietary AI algorithms, our SEO strategy implementation delivers improved SERP results more swiftly than any other SEO agency globally.</p>

</li>

<li>

<h6>NAVIGATING GOOGLE’S ALGORITHM CHANGES</h6>

<p>Google undergoes over 5000 changes to its algorithm each year. We leverage AI to empower our clients to seamlessly adapt to Google’s algorithmic shifts. Yes, our AI monitors Google’s AI.</p>

</li>

<li>

<h6>ENHANCED CUSTOMER JOURNEY</h6>

<p>Our AI SEO systems also contribute to optimizing customer journeys, enhancing user experiences, and maximizing ROI from marketing efforts.</p>

</li>

<li>

<h6>HUMAN-BASED THINKING</h6>

<p>Implementing advanced technical SEO operations such as chatbots and trend line identification greatly assists online businesses in providing human-based customer support. This, in turn, boosts revenue streams for online enterprises.</p>

</li>

<li>

<h6>PRECISE ROI TRACKING</h6>

<p>AI-based SEO ensures precise ROI tracking, involving real-time data tracking with high-level insights. Business owners can effectively measure SEO success with this approach.</p>

</li>

<li>

<h6>ENHANCED USER EXPERIENCE</h6>

<p>Leveraging artificial intelligence in search engine optimization allows website owners to elevate user experiences on their sites. Advanced data analytics discern user behavior and study patterns to enhance the intent of search queries, resulting in higher search engine rankings.</p>

</li>

<li>

<h6>PROACTIVE REPORTING</h6>

<p>Artificial intelligence aids in creating real-time reporting tables, correlating essential data and statistics for a powerful SEO campaign. Real-time reporting is crucial for running an effective SEO strategy.</p>

</li>

<li>

<h6>INTENT SATISFACTION</h6>

<p>Semantic engineering and information retrieval contribute to achieving proper intent satisfaction. This fundamental algorithm not only aligns with Google’s core principles but also ensures high-ranking positions for challenging keywords.</p>

</li>

<li>

<h6>PERFORMANCE GUARANTEE</h6>

<p>Advanced SEO strategies guarantee that a campaign achieves performance benchmarks, encompassing aspects such as keyword ranking, SERP visibility, organic traffic, brand value, and more.</p>

</li>

</ul>

</div>

<h2>SERVICES BY THATWARE</h2>

<p>There are numerous technical SEO companies worldwide, but Thatware stands out as the global leader with its AI-powered advanced SEO systems. From advanced off-page services to professional AI SEO solutions, no other agency possesses <strong>Thatware’s</strong> capabilities.</p>

<p>What distinguishes us is the 753 AI algorithms we’ve developed over the past 8 years. These AI tools provide cutting-edge technology, leveraging sophisticated data science, machine learning, semantic engineering, advanced search, and more.</p>

<p>In addition to AI SEO, the team at <strong>THATWARE</strong> offers a diverse range of services. Explore the options below and reach out to us to elevate your online marketing to a whole new level.</p>

</div>

<h2>ADVANCED SEO</h2>

<p>Experience the best practices of top-notch SEO standards with the perfect blend of search engineering. Our service model incorporates all our AI techniques, seamlessly integrating them with SEO to achieve greater success.</p>

</div>

<h2>Advanced Digital Marketing</h2>

<p>Utilizing the best data science practices for ROI-based marketing, we provide advanced data-driven strategies and cutting-edge techniques that can transform a small startup into a branded organization.</p>

</div>

<h2>Advanced Link Building</h2>

<p>Includes the implementation and execution of sophisticated link-building principles and strategies. Our advanced, cutting-edge link-building methods and techniques can contribute to achieving success.</p>

</div>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-braille”></i></div>–>

<!– <h2>AI Based search engine optimization</h2>–>

<!– <p>This is our unique service proposition. We make use of semantic engineering and NLP for getting standardized SEO’s implemented. In this model we offer many of our own invented artificial intelligence strategies.</p>–>

<!– <a href=”https://thatware.co/ai-based-seo-services/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-bold”></i></div>–>

<!– <h2>Branding, Paid Marketing</h2>–>

<!– <p>If you want to take your business into a whole new level with the help of paid marketing strategies like PPC, PR, Media, and etc. reach out to us. We will help you to reach your goal with our marketing strategies.</p>–>

<!– <a href=”https://thatware.co/branding-press-release-services/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-cc-mastercard”></i></div>–>

<!– <h2>Business Intelligence</h2>–>

<!– <p>Service which will help to uncover all the competitive best practices and strategies using data driven strategies and technicalities. We have an expert team of Business Intelligence to serve.</p>–>

<!– <a href=”https://thatware.co/business-intelligence-services/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-cubes”></i></div>–>

<!– <h2>Managed search engine optimization</h2>–>

<!– <p>This service model is designed and crafted for big companies and agencies. Example – Fortune companies, MNC’s and multi-national agencies. Here all the operations are custom and dedicatedly performed.</p>–>

<!– <a href=”https://thatware.co/managed-seo/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-area-chart”></i></div>–>

<!– <h2>One Time search engine optimization</h2>–>

<!– <p>This service model is designed and crafted for small businesses and start-ups. One-time SEO will help with getting all low hanging fruits resolved at best pricing which gives a positive effect on Digital Marketing.</p>–>

<!– <a href=”https://thatware.co/starter-seo-services/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<!–<div class=”thatware-services”>–>

<!– <div class=”serv-icon”><i class=”fa fa-handshake-o”></i></div>–>

<!– <h2>Reseller search engine optimization</h2>–>

<!– <p>This service model is designed and crafted for agencies who are looking to outsource their existing projects at affordable re-seller pricing. Our expert team of outsourcing can help to get the best deal out of the project.</p>–>

<!– <a href=”https://thatware.co/reseller-seo-services/” class=”shutter-btn”>Read More</a>–>

<!–</div>–>

<a class=”serv-btn shutter-btn” href=”https://thatware.co/services/“>VIEW ALL SERVICES</a>

</div>

<h2>Let Our Recognition Speak</h2>

</div>

<p>Brighton</p>

</div>

</div>

<p>Forbes</p>

</div>

<h2>CASE STUDIES</h2>

</div>

<h2>SUNRAY OPTICAL INC. (HEAVYGLARE EYEWEAR)</h2>

<p>Services Provided : Advanced SEO</p>

<p>The business marketing SEO model implemented for the above campaign is an advanced model. In other words, we executed top-notch search strategies with the right blend of Artificial Intelligence semantics, data science, advanced link building, and NLP. As a result, the following statistics were obtained:</p>

<ul>

<li><strong>1.5<sup>$MILLION</sup></strong><br/>In Sales</li>

<li><strong>600,000<sup>+</sup></strong><br/>Organic Sessions</li>

<li><strong>Over 50,00</strong><br/>Checkouts</li>

</ul>

</div>

<div class=”case-btn text-center”><a class=”shutter-btn” href=”https://thatware.co/case-studies/“>Read Complete Case</a></div>

</div>

<h2>VIDEO TESTIMONIALS</h2>

</div>

</div>

</div>

</div>

</div>

<h2>OUR CLIENTS</h2>

<p>We are acknowledged as one of the leading SEO firms globally. With that said, we provide the best SEO services tailored for both small businesses and large Fortune companies. Our clientele spans from local vendors to international billion-dollar corporations. Our SEO strategies for online businesses are advanced, and we offer tailor-made customized solutions to all our clients.</p>

<p>If you are seeking to hire a technical SEO expert or a search engine optimization consultant, THEN THATWARE is the right place for you. Our unique USP is one of a kind, making us the only advanced search company in the world that operates on AI and data science.</p>

<p>Allow us to serve you and elevate your online business to a whole new level. Become a part of our amazing portfolio. Join our family, where we work day and night to ensure the best-in-class service for your online needs.</p>

</div>

<ul>

</ul>

</div>

<li><a class=”shutter-btn” href=”https://thatware.co/faq/“>Generic FAQ’s</a></li>

</ul>

<!–<h2 class=”text-center”>FREQUENTLY ASKED QUESTIONS</h2>–>

</div>

<h3>SEND US YOUR THOUGHTS</h3>

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<p><label> <span class=”wpcf7-form-control-wrap” data-name=”menu-219″><select aria-invalid=”false” aria-required=”true” class=”wpcf7-form-control wpcf7-select wpcf7-validates-as-required” name=”menu-219″><option value=””>Choose service</option><option value=”Advanced SEO”>Advanced SEO</option><option value=”AI Based SEO”>AI Based SEO</option><option value=”Advanced Digital Marketing”>Advanced Digital Marketing</option><option value=”Fully Managed SEO”>Fully Managed SEO</option><option value=”Web Design and Dev”>Web Design and Dev</option><option value=”Graphic Designing”>Graphic Designing</option><option value=”Advanced Link Building”>Advanced Link Building</option><option value=”Software Development”>Software Development</option><option value=”Conversion Rate Optimization”>Conversion Rate Optimization</option><option value=”Social Media Marketing”>Social Media Marketing</option><option value=”Google Penalty Recovery”>Google Penalty Recovery</option><option value=”Local SEO”>Local SEO</option><option value=”Content Marketing”>Content Marketing</option><option value=”Content Writing”>Content Writing</option></select></span> </label>

</p>

<p><label> <span class=”wpcf7-form-control-wrap” data-name=”menu-220″><select aria-invalid=”false” aria-required=”true” class=”wpcf7-form-control wpcf7-select wpcf7-validates-as-required” name=”menu-220″><option value=””>Select budget</option><option value=”$0 – $500″>$0 – $500</option><option value=”$500 -$1000″>$500 -$1000</option><option value=”$1000 – $1500″>$1000 – $1500</option><option value=”$1500 – $2500″>$1500 – $2500</option><option value=”$2501 – $3500″>$2501 – $3500</option><option value=”$3501 -$5000″>$3501 -$5000</option><option value=”$5001 -$10000″>$5001 -$10000</option><option value=”$10000 +”>$10000 +</option></select></span> </label>

</p>

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<h2>GET IN TOUCH</h2>

<p>Fill out the contact form to reach our internet marketing experts in our company. If you want to inquire about affordable SEO packages or any other customized needs, please get in touch. We value and respond to each and every request that comes our way.</p>

</div>

</article>

</div><!– #mid –>

<footer class=”to-move narrow classic footer-separator” id=”footer”><div class=”foot-widgets five-col dark-sidebar”><div class=”foot-content clearfix”><div class=”widget widget_text” id=”text-26″><h3 class=”title”><span>Important Resources</span></h3> <div class=”textwidget”><p><a href=”https://thatware.co/privacy-policy/“>Privacy Policy</a><br>

<a href=”https://thatware.co/html-sitemap/“>HTML Sitemap</a><br>

<a href=”https://thatware.co/sitemap.xml“>XML Sitemap</a><br>

<a href=”https://thatware.co/whitepaper/“>WHITEPAPER</a><br>

<a href=”https://thatware.co/wp-content/uploads/2022/04/ThatWare-Corporate-Profile.pdf“>Company Deck</a><br>

<a href=”https://thatware.co/case-studies/“>Case Studies</a><br/>

<a href=”https://thatware.co/ai-implementations-seo/“>AI Implementations SEO</a><br/>

<a href=”https://thatware.co/ai-based-seo-blueprint/“>AI SEO Blueprint</a><br/>

<a href=”https://thatware.co/wp-content/uploads/2020/10/Mybettingdeals.com-Latest-Algorithm-update-presentation.pptx“>Algorithm Audit Sample</a><br/>

<a href=”https://www.youtube.com/watch?v=yE6bx2W3GU8“>AI-SEO Video</a></br></br></br></br></br></p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>SEO KNOWLEDGE BASE</p>

<ul>

<li><a href=”https://thatware.co/google-indexifembedded-tag/“>New Robot’s Tag</a><a href=”https://thatware.co/advanced-seo/“> </a></li>

<li><a href=”https://thatware.co/linked-domains/“>Linked Domains</a><a href=”https://thatware.co/advanced-seo/“> </a><br/>

<a href=”https://thatware.co/social-media-poll/“>Social Media Polling </a><br/>

<a href=”https://thatware.co/image-geotagging/“>Image Geo-tagging </a></li>

<li><a href=”https://thatware.co/topic-cluster/“>Topic Clustering</a></li>

<li><a href=”https://thatware.co/google-page-title-update/“>Google Title Re-write</a><br/>

<a href=”https://thatware.co/google-story/“>Google Story </a></li>

<li><a href=”https://thatware.co/author-schema/“>Author Schema</a></li>

<li><a href=”https://thatware.co/faq-schema/“>FAQ Schema</a></li>

<li><a href=”https://thatware.co/xml-audit/“>XML Audit</a></li>

<li><a href=”https://thatware.co/google-discover/“>Google Discover</a></li>

<li><a href=”https://thatware.co/favicon-optimization/“>Favicon Optimization</a></li>

<li><a href=”https://thatware.co/content-syndication/“>Content Syndication</a></li>

<li><a href=”https://thatware.co/google-analytics-goals/“>Goals in GA</a></li>

<li><a href=”https://thatware.co/domain-age/“>Domain Age</a></li>

<li><a href=”https://thatware.co/multilingual-seo/“>Multilingual SEO</a></li>

<li><a href=”https://thatware.co/share-of-voice/“>Share of Voice</a></li>

<li><a href=”https://thatware.co/url-inspection-api/“>URL INSPECTION API</a></li>

<li><a href=”https://thatware.co/sitewide-links/“>Sitewide Links</a></li>

<li><a href=”https://thatware.co/pogo-sticking/“>Pogo Sticking</a></li>

<li><a href=”https://thatware.co/crawler-directives/“>Crawler Directives</a></li>

<li><a href=”https://thatware.co/geographic-modifiers/“>Geographic Modifiers</a></li>

<li><a href=”https://thatware.co/5xx-status-codes/“>5xx Status Codes</a></li>

</ul>

<p style=”font-size: 13px; font-weight: 800; color: white;”>CORA SEO Sample</p>

<p><a href=”https://thatware.co/wp-content/uploads/2020/11/cora.pptx“>Download CORA report</a></p>

</div>

</div><div class=”widget widget_text” id=”text-27″><h3 class=”title”><span>Our Exclusive Services</span></h3> <div class=”textwidget”><p><a href=”https://thatware.co/digital-marketing-services/“>Digital Marketing</a><br/>

<a href=”https://thatware.co/link-building-services/“>Advanced Link Building</a><br/>

<a href=”https://thatware.co/advanced-seo-services/“>Advanced SEO</a><br/>

<a href=”https://thatware.co/ai-based-seo-services/“>AI Based SEO</a><br/>

<a href=”https://thatware.co/branding-press-release-services/“>Paid Marketing</a><br/>

<a href=”https://thatware.co/business-intelligence-services/“>Business Intelligence</a><br/>

<a href=”https://thatware.co/managed-seo/“>Fully Managed SEO</a><br/>

<a href=”https://thatware.co/conversion-rate-optimization/“>Conversion Funnel</a><br/>

<a href=”https://thatware.co/social-media-marketing/“>Social Media (SMM)</a><br/>

<a href=”https://thatware.co/google-penalty-recovery/“>Penalty Recovery</a><br/>

<a href=”https://thatware.co/local-business-seo-services/“>Local SEO (GMB)</a><br/>

<a href=”https://thatware.co/reseller-seo-services/“>Reseller SEO</a><br/>

<a href=”https://thatware.co/content-writing-services/“>Content Writing</a><br/>

<a href=”https://thatware.co/content-proofreading-services/“>Content Proofreading</a><br/>

<a href=”https://thatware.co/seo-consulting-services/“>SEO Consultation</a><br/>

<a href=”https://thatware.co/web-development-services/“>Web Development</a><br/>

<a href=”https://thatware.co/website-design-services/“>Web Designing</a><br/>

<a href=”https://thatware.co/chatbot-services/“>Chatbot Development</a><br/>

<a href=”https://thatware.co/ui-services/“>UI Development</a><br/>

<a href=”https://thatware.co/ux-services/“>UX Development</a><br/>

<a href=”https://thatware.co/app-development-services/“>App Development</a><br/>

<a href=”https://thatware.co/software-development-services/“>Software Development</a><br/>

<a href=”https://thatware.co/bug-testing-services/“>Bug Testing</a><br/>

<a href=”https://thatware.co/website-maintenance-services/“>Website Maintenance</a><br/>

<a href=”https://thatware.co/aws-managed-services/“>AWS Management</a><br/>

<a href=”https://thatware.co/technology-consulting-services/“>Tech Consultation</a><br/>

<a href=”https://thatware.co/graphic-design-services/“>Graphic Designing</a><br/>

<a href=”https://thatware.co/competitor-keyword-analysis/“>Competitor Research</a><br/>

<a href=”https://thatware.co/market-research-services/“>Market Research</a><br/>

<p><a href=”https://thatware.co/wp-content/uploads/2021/06/Extensive-SEO-Strategy.pptx“>Extensive SEO</a><br/>

<a href=”https://thatware.co/wp-content/uploads/2021/06/ThatWare-WorkFlow-Process.pptx“>Standard SEO</a><br/>

<a href=”https://thatware.co/wp-content/uploads/2021/06/ThatWare-AI-SEO-WorkFlow.pptx“>AI-Driven SEO</a><br/>

<a href=”https://thatware.co/wp-content/uploads/2021/06/Backlink-Extensive-Plan.pptx“>Backlink Technique’s</a><br/>

<a href=”https://thatware.co/wp-content/uploads/2021/06/ASO-Marketing-Strategy.pptx“>ASO WorkFlow</a></p>

</div>

</div><div class=”widget widget_text” id=”text-28″><h3 class=”title”><span>SEO Services by Industry</span></h3> <div class=”textwidget”><p><a href=”https://thatware.co/igaming-casino-seo-services/“>iGaming & Casino SEO</a><br/>

<a href=”https://thatware.co/accounting-firms-seo-services/“>SEO for Financial Industry</a><br/>

<a href=”https://thatware.co/law-firm-seo-services/“>SEO for legal services</a><br/>

<a href=”https://thatware.co/weight-loss-diet-seo-services/“>SEO for weight loss</a><br/>

<a href=”https://thatware.co/travel-seo-services/“>SEO for Travel</a><br/>

<a href=”https://thatware.co/real-estate-seo-services/“>SEO for real estate</a><br/>

<a href=”https://thatware.co/ecommerce-seo-services/“>SEO for ecommerce</a><br/>

<a href=”https://thatware.co/forex-seo-services/“>SEO for Forex</a><br/>

<a href=”https://thatware.co/cryptocurrency-seo-services/“>SEO for Crypto</a><br/>

<a href=”https://thatware.co/pharmaceutical-seo-services/“>SEO for Pharma</a><br/>

<a href=”https://thatware.co/seo-for-healthcare-industry/“>SEO for Health care</a><br/>

<a href=”https://thatware.co/dating-swinger-social-seo-services/“>SEO for Dating</a><br/>

<a href=”https://thatware.co/seo-services-website-security/“>SEO for Cyber Security</a></p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Media Links</p>

<p><a href=”http://blog.stevieawards.com/blog/artificial-intelligence-advances-digital-marketing”>Stevie Awards</a><br/>

<a href=”https://www.hindustantimes.com/brand-post/thatware-pioneers-advanced-seo-with-ai-powered-digital-arketing/story-2Y5Yc80BBjUIJl3tOhiyOL.html”>Hindustan Times</a><br/>

<a href=”https://economictimes.indiatimes.com/tech/software/meet-tuhin-banik-guy-whos-changing-the-digital-marketing-landscape-using-artificial-intelligence/articleshow/68609758.cms”>Economic Times</a><br/>

<a href=”https://timesofindia.indiatimes.com/business/india-business/this-is-how-digital-marketing-is-getting-changed-with-the-help-of-artificial-intelligence/articleshow/69229197.cms”>Times Of India</a><br/>

<a href=”https://www.forbesindia.com/article/brand-connect/thatware-is-redefining-digital-marketing-with-artificial-intelligence/55681/1″>Forbes</a><br/>

<a href=”https://www.dailypioneer.com/2020/state-editions/tuhin-banik-bags-bronze-medal-at-int—l-business-awards-in-austria.html”>Pioneer</a><br/>

<a href=”https://www.businesswireindia.com/thatware-llp-plans-major-global-expansion-as-ai-based-digital-marketing-witnesses-unprecedented-growth-65587.html”>Business Wire</a></p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Verifications</p>

</div>

</div><div class=”widget widget_text” id=”text-30″><h3 class=”title”><span>Indian State Based SEO Services</span></h3> <div class=”textwidget”><p><a href=”https://thatware.co/seo-company-india/“>SEO Services India</a><br/>

<a href=”https://thatware.co/seo-company-chennai/“>SEO Services Chennai</a><br/>

<a href=”https://thatware.co/seo-company-delhi/“>SEO Services Delhi</a><br/>

<a href=”https://thatware.co/seo-company-gurgaon/“>SEO Services Gurgaon</a><br/>

<a href=”https://thatware.co/seo-company-hyderabad/“>SEO Services Hyderabad</a><br/>

<a href=”https://thatware.co/seo-services-kolkata/“>SEO Services Kolkata</a><br/>

<a href=”https://thatware.co/seo-company-lucknow/“>SEO Services Lucknow</a><br/>

<a href=”https://thatware.co/seo-company-mumbai/“>SEO Services Mumbai</a><br/>

<a href=”https://thatware.co/seo-company-noida/“>SEO Services Noida</a><br/>

<a href=”https://thatware.co/seo-company-pune/“>SEO Services Pune</a><br/>

<a href=”https://thatware.co/seo-services-bangalore/“>SEO Services Bangalore</a><br/>

<a href=”https://thatware.co/seo-services-gujarat/“>SEO Services Gujarat</a><br/>

<a href=”https://thatware.co/seo-services-ahmedabad/“>SEO Ahmedabad</a><br/>

<a href=”https://thatware.co/seo-services-surat/“>SEO Services Surat</a><br/>

<a href=”https://thatware.co/seo-company-tamil-nadu/“>SEO Services Tamil Nadu</a></p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Country Based SEO Services</p>

<p><a href=”https://thatware.co/seo-services-australia/“>SEO Services Australia</a><br/>

<a href=”https://thatware.co/seo-services-canada/“>SEO Services Canada</a><br/>

<a href=”https://thatware.co/seo-services-europe/“>SEO Services Europe</a><br/>

<a href=”https://thatware.co/seo-services-israel/“>SEO Services Israel </a><br/>

<a href=”https://thatware.co/seo-services-new-zealand/“>SEO Services NZ</a><br/>

<a href=”https://thatware.co/seo-services-south-africa/“>SEO Services Africa</a><br/>

<a href=”https://thatware.co/seo-services-uae/“>SEO Services UAE</a><br/>

<a href=”https://thatware.co/seo-services-uk/“>SEO Services UK</a><br/>

<a href=”https://thatware.co/seo-services-usa/“>SEO Services USA</a></p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>DIY SEO Practices</p>

<a href=”https://thatware.co/google-my-business/“>DIY GMB Guide</a><br/>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Our Strategy Partner</p>

<p><a href=”https://thatware.io/”>THATWARE IO</a></p>

<p><a href=”https://www.webtool.co/”>WebTool</a></p>

</div>

</div><div class=”widget widget_text” id=”text-21″><h3 class=”title”><span>ADDress</span></h3> <div class=”textwidget”><p style=”font-size: 13px; font-weight: 800; color: white;”>India Office 1:</p>

<div class=”contact-address”>ThatWare LLP, Arunava Sarani, Sukriti Apartment – G Floor, North Ghosh Para, Bally, Howrah – 711227 Nearest Landmark:- Bally Halt Utsav Lodge</div>

<p style=”font-size: 13px; font-weight: 800; color: white;”>India Office 2:</p>

<div class=”contact-address”>ThatWare LLP, DC Neogi Road, Srijoni Apartment – G Floor, North Ghosh Para, Bally, Howrah – 711227</div>

<div class=”contact-email”><i class=”fa fa-envelope”></i><a href=”info@thatware.co”>info@thatware.co</a></div>

<div>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Dubai ADDRESS :</p>

<p>Al Asayel St – Al Furjan – Dubai – United Arab Emirates</p>

<p>GMZ-F01-104, Danube Gemz</p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>UK ADDRESS :</p>

<p>71-75 Shelton Street Covent Garden London WC2H 9JQ ENGLAND</p>

</div>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Our AI-SEO technology is Patent protected with Patent Filing Number: 202131021713</p>

<p style=”font-size: 13px; font-weight: 800; color: white;”>Thatware LLP is also protected under Intellectual property: CBR IP 6979</p>

</div>

</div></div>

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get_text(): This is a method provided by BeautifulSoup that extracts all the text from the HTML content stored in the soup variable

[ ]

# Extract the text from the webpage.

text = soup.get_text()

text

text.split(): This splits the text into a list of words, using any amount of whitespace (spaces, tabs, newlines) as the delimiter.

text=” Welcome to my website \n\nThis is a paragraph of text. “

text.split()= [“Welcome”, “to”, “my”, “website”, “This”, “is”, “a”, “paragraph”, “of”, “text.”]

‘ ‘.join(…): This joins the list of words back into a single string, using a single space character as the separator.

Then, the join() method will join the list of words back into a single string, using a single space character as the separator:

clean_text = ‘ ‘.join(text.split())

clean_text = “Welcome to my website This is a paragraph of text.”

[ ]

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

clean_text

CountVectorizer: A Tool to Convert Text to Numbers.

In the world of data science, machine learning, and SEO-driven content analysis, text must be transformed into numerical form before it can be analyzed by algorithms. One of the most widely used tools for this purpose in Python is CountVectorizer. It provides a simple yet powerful way to convert raw text into structured numerical data that computers can interpret and process effectively.

CountVectorizer plays a critical role in natural language processing (NLP) tasks such as keyword analysis, topic modeling, sentiment analysis, and search engine optimization. By converting words into counts, it lays the groundwork for understanding patterns, relevance, and emphasis within textual content.

What is CountVectorizer?

Imagine having a tool that can read through large volumes of text and keep track of how often each word appears. CountVectorizer does exactly that. It scans text documents, identifies unique words, and records their frequency of occurrence.

In technical terms, CountVectorizer converts a collection of text documents into a matrix of token counts. Each row represents a document, each column represents a unique word (or token), and each cell contains the number of times that word appears in the document.

This numerical representation allows machines to analyze text statistically, making it a foundational component of many NLP and SEO workflows.

Why CountVectorizer Matters in Text Analysis and SEO

Search engines rely on understanding textual patterns to determine relevance and ranking. CountVectorizer helps bridge the gap between human language and machine interpretation by translating content into data that algorithms can evaluate.

From an SEO perspective, CountVectorizer enables professionals to:

Identify frequently used keywords
Analyze keyword distribution across pages
Detect content gaps and over-optimization
Compare keyword usage between competitors
Support data-driven content strategies

By quantifying language, it becomes possible to move beyond intuition and rely on measurable insights.

Understanding Stop Words

What Are Stop Words?

Stop words are commonly used words that appear frequently in language but usually carry minimal semantic value. Examples include words such as “the,” “and,” “is,” “in,” and “on.” While these words are essential for sentence structure, they typically do not contribute meaningful insights for analytical tasks like keyword extraction or topic identification.

Including stop words in text analysis can skew results by inflating word counts without adding contextual value.

Removing Stop Words for Better Insights

By setting stop_words=’english’ in CountVectorizer, the tool automatically filters out a predefined list of common English stop words. This allows the analysis to focus on more meaningful and content-rich terms.

Removing stop words improves:

Keyword relevance
Topic clarity
Signal-to-noise ratio in analysis
SEO-focused keyword extraction

This step ensures that attention is placed on words that truly define the subject matter of the content.

Simple Example of Stop Word Removal

Consider the sentence:
“The cat sits on the mat.”

Without removing stop words, terms like “the” and “on” would be included in the analysis. However, these words do not add significant meaning.

After removing stop words, the focus shifts to:

cat
sits
mat

These terms provide a clearer understanding of the sentence’s subject and intent.

Fitting and Transforming Text into a Bag of Words Model

One of the most important concepts when working with CountVectorizer is understanding the difference between fitting and transforming text.

The process is commonly written as:

X = vectorizer.fit_transform([text])

Although simple in appearance, this line performs two crucial steps.

What Does “Fit” Mean?

Fitting refers to the learning phase. During this step, CountVectorizer scans the text and identifies all unique words after preprocessing steps such as lowercasing and stop word removal.

This step builds a vocabulary, which is essentially a dictionary of all distinct tokens found in the text.

For example, if the text is:
“Cat mat cat mat cat”

The fitting process identifies:

cat
mat

These words become the features of the model.

What Does “Transform” Mean?

Transforming is the conversion phase. Once the vocabulary is established, the tool counts how many times each word appears in the text and converts the results into numerical form.

Using the previous example:

cat appears 3 times
mat appears 2 times

The transformed output becomes a numerical representation similar to:

{ “cat”: 3, “mat”: 2 }

This numeric format is what allows algorithms to analyze and compare text efficiently.

Understanding the Bag of Words Representation

The output of CountVectorizer is commonly referred to as a Bag of Words (BoW) model. In this model:

Word order is ignored
Grammar is disregarded
Only word frequency matters

While this may seem simplistic, the Bag of Words approach is highly effective for tasks such as keyword analysis, document classification, and SEO auditing.

By focusing on frequency, BoW highlights which terms dominate a piece of content and which topics receive the most emphasis.

Working with Multiple Documents

CountVectorizer is not limited to single text inputs. When applied to multiple documents, it creates a matrix where:

Each row represents a document
Each column represents a unique word across all documents

This capability makes it ideal for analyzing entire websites, blog collections, or competitor content at scale.

For SEO teams, this enables:

Page-to-page keyword comparison
Identification of overlapping or missing keywords
Content clustering and topic modeling

Enhancing CountVectorizer with N-Grams

By default, CountVectorizer analyzes single words (unigrams). However, it can be extended to capture n-grams, which are sequences of multiple words.

For example:

Unigrams: search, engine, optimization
Bigrams: search engine, engine optimization
Trigrams: search engine optimization

N-grams provide deeper context and are particularly valuable for:

Long-tail keyword targeting
Voice search optimization
Capturing user intent more accurately

This enhancement allows SEO professionals to move beyond isolated words and focus on meaningful keyword phrases.

Limitations of CountVectorizer

While CountVectorizer is powerful, it does have limitations that should be understood:

It does not account for word meaning or synonyms
It treats all words as equally important
It ignores semantic relationships

For example, words like “SEO” and “search optimization” are treated as separate terms, even though they convey similar meaning.

Because of these limitations, CountVectorizer is often paired with more advanced techniques such as TF-IDF, word embeddings, or semantic analysis for deeper insights.

[ ]

# Use CountVectorizer to create the BoW model, removing stop words

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

<1×773 sparse matrix of type ‘<class ‘numpy.int64′>’

with 773 stored elements in Compressed Sparse Row format>

Converting the Bag of Words Model to a DataFrame

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

Introduction to DataFrames

In data analysis, a DataFrame is a powerful data structure provided by libraries like Pandas in Python. It allows for easy manipulation, analysis, and visualization of data in a tabular format. Converting the Bag of Words (BoW) model into a DataFrame makes it easier to handle and analyze text data, providing a structured view of word frequencies and their distribution.

Transforming BoW Output

To convert the Bag of Words model output into a DataFrame, follow these steps:

Create the BoW Model:

Begin by applying the Bag of Words model to your text data. This involves tokenizing the text and counting the frequency of each word. Use libraries like Scikit-learn to generate a matrix of word counts, where rows represent documents and columns represent words.

Generate the DataFrame:

Once you have the word count matrix, import the Pandas library and create a DataFrame. Each row of the DataFrame corresponds to a document, and each column represents a specific word from the BoW model. The cell values are the frequencies of the words in the corresponding documents.

Labeling and Indexing:

Label the DataFrame columns with the unique words identified by the BoW model and set the index to represent different documents or text samples. This labeling helps in easily identifying and accessing specific keywords and their frequencies.

Analyzing the DataFrame:

With the DataFrame created, you can perform various analyses, such as sorting words by frequency, identifying the most common keywords, and visualizing word distributions. This structured format allows for efficient data manipulation and insightful analysis.

Example:

Consider the text “apple banana apple orange”. After processing, the resulting DataFrame could appear as follows:

This table indicates that “apple” occurs 2 times, “banana” 1 time, and “orange” 1 time.

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

X.toarray()
- What It Does: Converts the BoW matrix into a 2D NumPy array.
- Why It’s Needed: The BoW model created by CountVectorizer is in a sparse matrix format, which is efficient for storing data but not very readable. toarray() converts it into a regular 2D array where each row represents a document, and each column represents a word.

Example:

Suppose you have the following simple text document:
“apple banana apple”
- The BoW model might be represented as a sparse matrix like this:
  [[2, 1]]
2 is the count for “apple” and 1 is the count for “banana”.

After toarray(), it becomes:
[[2, 1]]
vectorizer.get_feature_names_out()

What It Does: Retrieves the list of words (features) that the CountVectorizer used.
Why It’s Needed: This list of words is used to label the columns of the DataFrame. Each column corresponds to one of these words.
Example:

If your vocabulary from the text document was [‘apple’, ‘banana’], get_feature_names_out() will return:

[‘apple’, ‘banana’]

pd.DataFrame(…)
- What It Does: Creates a DataFrame using the 2D array and the list of feature names.
- Why It’s Needed: A DataFrame is a tabular format that’s easier to read and manipulate compared to raw arrays. Each column represents a word, and each row represents a document (though here we have just one document).

Example:
- With the 2D array [[2, 1]] and the feature names [‘apple’, ‘banana’], the DataFrame will look like this:

[ ]

# Convert the BoW model into a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

bow_df

bow_df.sum(axis=0)

Understanding axis=0

The axis parameter in pandas determines the direction along which a function like sum() operates.

axis=0: This means you want to sum down the columns. So, for each column (which represents a word), you add up all the values in that column.
axis=1: This would sum across the rows. In this case, it would add up all the word counts in each row (which is not what we want here).

Why Use sum(axis=0)?

In the BoW (Bag of Words) model, each column of the DataFrame represents the count of a particular word. Since we’re interested in knowing how often each word appears in the entire document (or across multiple documents if there were more rows), we need to add up the counts in each column.

Let’s say you have a DataFrame that looks like this:

If you call bow_df.sum(axis=0), you are summing each column’s values:

For the “apple” column: The sum is 2.
For the “banana” column: The sum is 1.
For the “orange” column: The sum is 1.
So, the result of sum(axis=0) would be:

This tells you that “apple” appears 2 times, “banana” 1 time, and “orange” 1 time in the text. The axis=0 is crucial here because it sums down the columns to give you the total count of each word across the entire text.

Summary

axis=0: Adds up values in each column (which represents each word) to get the total frequency of that word across the entire text.
This is important because it helps us find out which words are most common in the text, which can then be used to analyze keyword usage for SEO purposes.

[ ]

# Sort words by frequency in descending order

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

word_frequencies

[ ]

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

# Step 1: Extract Text Content from a Website

def get_website_text(url):

try:

# Send an HTTP request to the website

response = requests.get(url)

# Parse the website’s HTML content

soup = BeautifulSoup(response.content, ‘html.parser’)

# Extract the text from the webpage

text = soup.get_text()

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

return clean_text

except Exception as e:

print(f”Failed to retrieve content from {url}: {e}”)

return None

# Step 2: Create the BoW Model without Stop Words

def create_bow_model(text):

# Use CountVectorizer to create the BoW model, removing stop words

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

# Convert the BoW model into a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

return bow_df

# Step 3: Analyze Word Frequencies

def analyze_word_frequencies(bow_df):

# Sort words by frequency in descending order

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

# Print the most common keywords (excluding stop words)

print(“Most Common Keywords:\n”)

print(word_frequencies.head(50)) # Top 50 most frequent meaningful words

return word_frequencies

# Step 4: Main Function to Run the Analysis

def analyze_website_content(url):

print(f”Analyzing content from: {url}\n”)

# This line calls the get_website_text(url) function to extract all the text from the specified website.

text_content = get_website_text(url)

if text_content: # This line checks if the text_content variable is not empty or None. This ensures that text was successfully retrieved from the website.

bow_df = create_bow_model(text_content) # This line calls the create_bow_model(text_content) function to convert the text into a Bag of Words (BoW) model.

analyze_word_frequencies(bow_df) #This line calls the analyze_word_frequencies(bow_df) function to find out which words are the most common in the text.

else:

print(“No content to analyze.”)

# Website URL for Analysis

website_url = ‘https://thatware.co/’ # ThatWare homepage

# Analyze the specified website

analyze_website_content(website_url)

Analyzing content from: https://thatware.co/

Most Common Keywords:

seo 122

services 52

ai 37

advanced 22

google 16

thatware 13

marketing 12

data 9

link 8

algorithms 8

content 8

development 8

building 8

based 8

search 8

algorithm 7

optimization 7

business 7

strategies 7

company 6

online 6

intelligence 6

page 6

media 6

digital 5

case 5

clients 5

science 5

strategy 5

best 5

diy 5

website 5

faq 5

time 4

agency 4

edge 4

nlp 4

artificial 4

provide 4

backlink 4

design 4

research 4

web 4

keyword 4

2024 4

site 4

india 4

success 4

contact 4

dtype: int64

Explanation of Changes

Using response.text for Content Retrieval:
The use of response.text allows the HTML content of a webpage to be retrieved as a Unicode string. This makes the text easier to process and manipulate compared to raw byte data. By working with Unicode text, the extraction process becomes more reliable, especially when handling diverse character sets commonly found across modern websites.

This approach ensures that the webpage content is accurately captured in a readable format before it is passed through further parsing and analysis steps.

Removing <script> and <style> Elements:
The revised approach introduces a cleaning step that removes <script> and <style> elements from the HTML document. These elements typically contain JavaScript logic and CSS styling rules, which are not part of the visible or meaningful textual content of a webpage.

By using BeautifulSoup’s decompose() method, these non-content elements are completely removed from the document tree. This ensures that embedded scripts, tracking codes, animations, and styling instructions do not interfere with text analysis or inflate keyword frequency counts with irrelevant tokens.

Improved Text Extraction:
Once unnecessary elements are removed, the cleaned HTML structure allows for more accurate text extraction using soup.get_text(). This method gathers only the readable text that users typically see on the webpage, such as headings, paragraphs, navigation labels, and call-to-action elements.

Additional text cleanup, such as removing excessive whitespace, line breaks, and redundant spacing, further refines the extracted content. The result is a streamlined body of text that is well-suited for natural language processing and keyword analysis.

Why These Changes Are Useful:

Cleaner and More Relevant Text Data

Removing scripts and styles dramatically improves the quality of extracted text. Without this step, the analysis might include function names, variable identifiers, or CSS class references that have no SEO or semantic relevance. Cleaner data leads to more trustworthy results and ensures that the analysis reflects actual content rather than technical clutter.

More Accurate Bag of Words (BoW) Analysis

The Bag of Words model relies entirely on word frequency. If irrelevant code or styling text is included, it can distort frequency counts and reduce the accuracy of insights. By focusing only on visible, meaningful content, the BoW model can highlight genuine keyword usage patterns, helping identify what a webpage truly emphasizes.

This precision is essential for keyword research, on-page SEO audits, and competitive analysis.

Enhanced Visualization and Interpretation

With cleaner text feeding into the BoW model, visual outputs such as word clouds and bar graphs become far more informative. High-frequency keywords displayed in these visuals accurately represent the website’s core messaging, services, and topical focus.

This allows SEO professionals and content strategists to quickly interpret dominant themes, spot optimization opportunities, and validate whether content aligns with intended search intent.

Strategic SEO Benefits of This Workflow

This structured approach to text extraction and analysis supports a wide range of SEO and content intelligence use cases. By automating content cleanup and keyword modeling, businesses can consistently audit multiple websites without manual intervention.

From an SEO perspective, this workflow helps:

Identify primary and secondary keywords
Detect overused or underutilized terms
Compare keyword emphasis across competitors
Support content gap and topical authority analysis
Guide data-driven content optimization strategies

The ability to process multiple URLs also makes this method scalable for agency-level audits and enterprise SEO projects.

Using Visual Outputs for Decision-Making

Word clouds provide a quick, high-level overview of keyword prominence, making them ideal for presentations and exploratory analysis. Bar graphs, on the other hand, offer precise comparisons of keyword frequency and are better suited for tactical decision-making.

When used together, these visualizations help bridge the gap between technical analysis and strategic insights, allowing both marketers and non-technical stakeholders to understand content performance clearly.

Positioning This Analysis for Advanced SEO

While the Bag of Words approach focuses on frequency rather than meaning, it serves as a strong foundation for more advanced techniques. Once keyword patterns are identified, the analysis can be extended using methods such as TF-IDF, semantic clustering, or intent-based optimization.

This layered approach ensures that businesses are not only targeting keywords but also building content relevance, topical depth, and long-term search visibility.

Ensuring Scalability and Performance in Website Analysis

When analyzing multiple websites or large-scale web pages, performance and scalability become critical considerations. Extracting and processing text efficiently ensures that the analysis remains reliable even as the number of URLs increases. Clean text extraction minimizes computational overhead by reducing unnecessary tokens, allowing the Bag of Words model to process only relevant data. This is especially important for agencies and enterprises that regularly audit dozens or even hundreds of pages.

By structuring the workflow into reusable functions, the analysis can be easily extended or integrated into automated SEO pipelines. This modular approach supports consistent audits, repeatable results, and faster turnaround times for strategic decision-making.

Handling Noise and Boilerplate Content

Modern websites often include repetitive elements such as navigation menus, footer links, cookie notices, and legal disclaimers. While these elements are visible to users, they may not always reflect the core topic of a page. When included in keyword analysis, such boilerplate content can skew results by repeatedly emphasizing generic terms.

Although basic HTML cleanup improves accuracy, advanced implementations may further refine extraction by isolating main content areas such as articles or service descriptions. Even without advanced segmentation, removing scripts and styles already significantly reduces noise, allowing the most meaningful keywords to surface more clearly.

Improving Keyword Prioritization

Raw keyword frequency alone does not determine SEO success. However, it provides a strong starting point for prioritization. High-frequency terms often indicate central themes, while medium-frequency terms may reveal supporting topics or niche opportunities.

By analyzing frequency distributions across multiple competitor websites, businesses can uncover patterns such as:

Keywords consistently emphasized across the industry
Terms unique to specific competitors
Underrepresented keywords with growth potential

This insight enables smarter content planning and more targeted keyword strategies

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[ ]

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

# Step 1: Extract Text Content from a Website

def get_website_text(url):

try:

response = requests.get(url)

soup = BeautifulSoup(response.content, ‘html.parser’)

# Extract text from the webpage

text = soup.get_text()

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

return clean_text

except Exception as e:

print(f”Failed to retrieve content from {url}: {e}”)

return None

# Step 2: Create the BoW Model without Stop Words

def create_bow_model(text):

# Use CountVectorizer with stop words removed

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

# Convert the BoW model to a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

return bow_df

# Step 3: Analyze Word Frequencies

def analyze_word_frequencies(bow_df):

# Sort words by frequency in descending order

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

# Print the most common words (excluding stop words)

print(“Most Common Keywords:\n”)

print(word_frequencies.head(20)) # Top 10 most frequent meaningful words

return word_frequencies

# Step 4: Main Function to Run the Analysis

def analyze_website_content(url):

print(f”Analyzing content from: {url}\n”)

text_content = get_website_text(url)

if text_content:

bow_df = create_bow_model(text_content)

analyze_word_frequencies(bow_df)

else:

print(“No content to analyze.”)

# Example URLs for Testing

websites = [‘https://thatware.co/’, # ThatWare homepage

‘https://www.seotechexperts.com/seo-agency-india.html’, # seotechexperts homepage

‘https://www.techwebers.com/’, # Techwebers homepage

‘https://www.incrementors.com/’ # incrementors homepage

]

# Test the code on example websites

for site in websites:

analyze_website_content(site)

print(“\n” + “=”*50 + “\n”)

Analyzing content from: https://thatware.co/

Most Common Keywords:

seo 122

services 52

ai 37

advanced 22

google 16

thatware 13

marketing 12

data 9

link 8

algorithms 8

content 8

development 8

building 8

based 8

search 8

algorithm 7

optimization 7

business 7

strategies 7

company 6

dtype: int64

==================================================

Analyzing content from: https://www.seotechexperts.com/seo-agency-india.html

Most Common Keywords:

seo 60

services 23

brand 14

provide 14

make 12

marketing 11

business 11

clients 10

experts 10

strategies 9

result 8

agency 7

industry 7

strategy 7

page 7

report 7

technical 7

project 7

professionals 7

time 6

dtype: int64

==================================================

Analyzing content from: https://www.techwebers.com/

Most Common Keywords:

seo 22

website 21

online 14

webers 12

tech 12

digital 10

help 8

divi 7

search 7

create 7

design 7

team 6

presence 6

marketing 6

web 6

engine 5

world 5

results 5

services 4

visibility 4

dtype: int64

==================================================

Analyzing content from: https://www.incrementors.com/

Most Common Keywords:

marketing 23

business 18

free 15

traffic 13

website 13

best 11

sales 11

services 10

strategy 10

digital 7

online 7

incrementors 7

customers 7

30 7

results 6

target 6

highly 6

minute 6

seo 6

team 6

dtype: int64

==================================================

1. Understanding the Content Focus

What It Means: The Bag of Words (BoW) analysis reveals the most frequently used keywords on a website, providing insight into the primary focus of the content.

Example: Analyzing the content from https://thatware.co/ shows that keywords like “SEO,” “services,” “AI,” and “advanced” appear most often. This suggests that the site emphasizes SEO services, AI technologies, and advanced strategies.

How It Helps:

Alignment with Business Goals: By examining if these keywords align with the site’s core offerings, the owner can verify whether the content matches their business focus. For example, if “SEO” and “AI” are primary services, the content is appropriately aligned. However, if the aim is to highlight different services, adjustments may be needed.

Attracting the Right Audience: Ensuring that the content features keywords relevant to what potential customers are searching for enhances the chances of attracting the right traffic.

2. Identifying Content Gaps or Overemphasis

What It Means: BoW analysis can pinpoint keywords that are either overly prominent or lacking in the content.

Example: If “SEO” appears 122 times while “digital marketing” only appears 5 times, this may indicate an overemphasis on SEO at the expense of other important services.

How It Helps:

Content Optimization: The website owner can balance keyword usage by increasing focus on underrepresented terms like “digital marketing” if it’s a key service. This adjustment helps create a more comprehensive content strategy.

Improving Keyword Density: The analysis aids in fine-tuning keyword density to avoid overuse (keyword stuffing) or underuse, ensuring that important terms are neither excessively repeated nor neglected.

3. Adjusting Content for Better User Engagement

What It Means: The BoW analysis can indicate whether the content effectively engages the target audience.

Example: Frequent keywords such as “strategy,” “best,” and “success” suggest that the content is geared towards strategic advice or success stories.

How It Helps:

Content Relevance: Adjusting content to align with audience interests ensures it remains relevant. For instance, if the objective is to generate more business inquiries, the content might need more actionable insights or strong calls to action.

User Retention: Content that effectively uses the right keywords can engage visitors longer, decreasing bounce rates and improving overall user retention.

[ ]

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

# Step 1: Extract Text Content from a Website

def get_website_text(url):

try:

response = requests.get(url)

# Parse the HTML content using BeautifulSoup with response.text

soup = BeautifulSoup(response.text, ‘html.parser’)

# Remove script and style elements to clean the content

for script_or_style in soup([‘script’, ‘style’]):

script_or_style.decompose()

# Extract the text content from the HTML

text = soup.get_text()

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

return clean_text

except Exception as e:

print(f”Failed to retrieve content from {url}: {e}”)

return None

# Step 2: Create the BoW Model without Stop Words

def create_bow_model(text):

# Use CountVectorizer with stop words removed

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

# Convert the BoW model to a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

return bow_df

# Step 3: Analyze Word Frequencies

def analyze_word_frequencies(bow_df):

# Sort words by frequency in descending order

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

# Print the most common words (excluding stop words)

print(“Most Common Keywords:\n”)

print(word_frequencies.head(20)) # Top 20 most frequent meaningful words

return word_frequencies

# Step 4: Main Function to Run the Analysis

def analyze_website_content(url):

print(f”Analyzing content from: {url}\n”)

text_content = get_website_text(url)

if text_content:

bow_df = create_bow_model(text_content)

analyze_word_frequencies(bow_df)

else:

print(“No content to analyze.”)

# Example URLs for Testing

websites = [‘https://thatware.co/’, # ThatWare homepage

‘https://www.wikipedia.org/’, # Wikipedia homepage

‘https://www.bbc.com/’, # BBC News homepage

‘https://www.nytimes.com/’ # New York Times homepage

]

# Test the code on example websites

for site in websites:

analyze_website_content(site)

print(“\n” + “=”*50 + “\n”)

Analyzing content from: https://thatware.co/

Most Common Keywords:

seo 122

services 52

ai 37

advanced 22

google 16

thatware 13

marketing 12

data 9

link 8

algorithms 8

content 8

development 8

building 8

based 8

search 8

algorithm 7

optimization 7

business 7

strategies 7

company 6

dtype: int64

==================================================

Analyzing content from: https://www.wikipedia.org/

Most Common Keywords:

ðºð 16

000 14

free 13

âµ 11

²à 10

¾à 10

èª 8

articles 8

ªà 7

ð¹ 7

wikipedia 7

bahasa 6

ð½ñ 5

english 5

ð²ð 5

à² 5

ðºñ 5

ina 4

ðµð 4

à½ 4

dtype: int64

==================================================

Analyzing content from: https://www.bbc.com/

Most Common Keywords:

hrs 38

days 16

says 10

mins 10

agous 9

olympics 9

paris 9

bbc 8

day 7

gender 6

olympic 6

town 5

medal 5

2024 5

ukraine 5

gold 5

family 5

week 5

new 4

trump 4

dtype: int64

==================================================

Analyzing content from: https://www.nytimes.com/

Most Common Keywords:

min 39

new 29

york 24

news 19

stories 14

world 11

included 10

learn 10

subscription 10

access 10

people 9

ideas 8

biggest 8

recommendations 8

day 7

make 7

tech 7

times 7

best 7

cooking 6

dtype: int64

==================================================

Explanation of Changes

Using response.text:
response.text retrieves the HTML content as a Unicode string, making it more convenient to process the text content of a webpage.

Removing <script> and <style> Elements:
The updated code now includes a loop that identifies and removes <script> and <style> elements using the decompose() method. This step ensures that JavaScript code and CSS styles, which are not part of the main content, are excluded from the text extraction process.

Text Extraction:
After cleaning the HTML content, the text is extracted using soup.get_text(), which provides a clearer focus on the actual content displayed on the webpage.

Why These Changes Are Useful:

Cleaner Text Extraction: By eliminating unnecessary elements like <script> and <style>, the extracted text more accurately reflects what users see on the page. This is essential for precise analysis, as it avoids the inclusion of irrelevant code or styles in your word frequency analysis.
Improved Analysis Results: With cleaner, more relevant text, the Bag of Words (BoW) model and subsequent word frequency analysis can concentrate on meaningful terms, enhancing the quality of insights derived from the content.

[ ]

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

import matplotlib.pyplot as plt

from wordcloud import WordCloud

# Step 1: Extract Text Content from a Website

def get_website_text(url):

try:

response = requests.get(url)

soup = BeautifulSoup(response.content, ‘html.parser’)

# Extract text from the webpage

text = soup.get_text()

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

return clean_text

except Exception as e:

print(f”Failed to retrieve content from {url}: {e}”)

return None

# Step 2: Create the BoW Model without Stop Words

def create_bow_model(text):

# Use CountVectorizer with stop words removed

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

# Convert the BoW model to a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

return bow_df

# Step 3: Generate Word Cloud from BoW Model

def generate_wordcloud(bow_df):

# Sum the occurrences of each word to get the total frequency

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

# Convert the word frequencies to a dictionary format

word_freq_dict = word_frequencies.to_dict()

# Generate the word cloud

wordcloud = WordCloud(width=800, height=400, background_color=’white’).generate_from_frequencies(word_freq_dict)

# Plot the word cloud

plt.figure(figsize=(10, 5))

plt.imshow(wordcloud, interpolation=’bilinear’)

plt.axis(‘off’) # Remove the axes

plt.show()

# Step 4: Main Function to Run the Analysis

def analyze_website_content(url):

print(f”Analyzing content from: {url}\n”)

text_content = get_website_text(url)

if text_content:

bow_df = create_bow_model(text_content)

generate_wordcloud(bow_df)

else:

print(“No content to analyze.”)

# Example URLs for Testing

websites = [

‘https://thatware.co/’, # ThatWare homepage

‘https://www.seotechexperts.com/seo-agency-india.html’, # SEO Tech Experts homepage

‘https://www.techwebers.com/’, # Techwebers homepage

‘https://www.incrementors.com/’ # Incrementors homepage

]

# Test the code on example websites

for site in websites:

analyze_website_content(site)

print(“\n” + “=”*50 + “\n”)

[ ]

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import CountVectorizer

import pandas as pd

import matplotlib.pyplot as plt

# Step 1: Extract Text Content from a Website

def get_website_text(url):

try:

response = requests.get(url)

soup = BeautifulSoup(response.content, ‘html.parser’)

# Extract text from the webpage

text = soup.get_text()

# Clean up the text by removing extra spaces, line breaks, etc.

clean_text = ‘ ‘.join(text.split())

return clean_text

except Exception as e:

print(f”Failed to retrieve content from {url}: {e}”)

return None

# Step 2: Create the BoW Model without Stop Words

def create_bow_model(text):

# Use CountVectorizer with stop words removed

vectorizer = CountVectorizer(stop_words=’english’)

# Fit and transform the text into a BoW model

X = vectorizer.fit_transform([text])

# Convert the BoW model to a DataFrame for easier analysis

bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

return bow_df

# Step 3: Generate Bar Graph from BoW Model

def plot_bow_graph(bow_df):

# Sum the occurrences of each word to get the total frequency

word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)

# Select the top 20 keywords

top_keywords = word_frequencies.head(20)

# Plotting the bar graph

plt.figure(figsize=(12, 8))

plt.barh(top_keywords.index, top_keywords.values, color=’skyblue’)

plt.xlabel(‘Frequency’)

plt.ylabel(‘Keywords’)

plt.title(‘Top 20 Keywords by Bag of Words Frequency’)

plt.gca().invert_yaxis() # Invert y-axis to show the highest frequencies at the top

plt.show()

# Step 4: Main Function to Run the Analysis

def analyze_website_content(url):

print(f”Analyzing content from: {url}\n”)

text_content = get_website_text(url)

if text_content:

bow_df = create_bow_model(text_content)

plot_bow_graph(bow_df)

else:

print(“No content to analyze.”)

# Example URLs for Testing

websites = [

‘https://thatware.co/’, # ThatWare homepage

‘https://www.seotechexperts.com/seo-agency-india.html’, # SEO Tech Experts homepage

‘https://www.techwebers.com/’, # Techwebers homepage

‘https://www.incrementors.com/’ # Incrementors homepage

]

# Test the code on example websites

for site in websites:

analyze_website_content(site)

print(“\n” + “=”*50 + “\n”)

Hiring ThatWare offers businesses access to strategic expertise, cost-effective solutions, and extensive experience across the digital marketing landscape. With a dedicated team skilled in executing high-performing search marketing campaigns, ThatWare delivers measurable SEO results. The company’s adaptability, data-driven approach, and commitment to continuous experimentation make it a strong partner for organizations seeking sustainable and effective SEO growth.