Get Better Keyword Clarity With TF-IDF Powered SEO

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The project’s primary purpose, “Keyword Clarity: TF-IDF Powered Web Keyword Extractor” is to help website owners identify and analyze the most important keywords on their web pages. Using the TF-IDF (Term Frequency-Inverse Document Frequency) method, the project pinpoints which words are significant within a webpage’s content The core objective of the project, “Keyword Clarity: TF-IDF Powered Web Keyword Extractor,” is to give website owners a precise understanding of what truly drives their content. Rather than relying on guesswork, this solution uses the proven TF-IDF (Term Frequency–Inverse Document Frequency) model to uncover the keywords that carry real weight within a webpage.

By evaluating how often a term appears on a page and comparing its relevance against a broader content set, the tool highlights words that genuinely define the page’s theme. This makes it easier to separate meaningful keywords from generic or overused terms that add little SEO value. As a result, content creators gain clear, data-backed insights into how search engines interpret their pages.

These insights play a vital role in refining on-page optimization strategies. With accurate keyword identification, website owners can strengthen content relevance, improve search engine rankings, and attract more targeted traffic. Ultimately, the project empowers businesses to align their content with user intent and search algorithms, leading to stronger visibility, higher engagement, and sustainable organic growth.

Get Better Keyword Clarity With TF-IDF Powered Web Keyword Extractor

What is TF-IDF?
TF-IDF stands for Term Frequency–Inverse Document Frequency. It is a statistical technique used to determine how relevant a specific word is within a single document when compared to a larger group of documents, often called a corpus. Instead of judging importance by frequency alone, TF-IDF balances usage with uniqueness. In simple terms, it helps identify words that truly define a piece of content by analyzing how often they appear and how uncommon they are across similar texts. This makes TF-IDF especially valuable for content analysis, keyword research, and search engine optimization, where understanding meaningful terms is essential.
Breaking Down TF-IDF
Term Frequency (TF):
Term Frequency measures how often a particular word appears within a document. The assumption is straightforward: if a word appears frequently, it is likely important to the topic being discussed. However, TF alone cannot determine true significance because common words may appear often without adding value. TF is usually calculated by dividing the number of times a word occurs by the total number of words in the document.
Example: If the word “cat” appears 10 times in a 100-word article, the TF value for “cat” would be 10 ÷ 100 = 0.1.
Inverse Document Frequency (IDF):
Inverse Document Frequency measures how rare or distinctive a word is across multiple documents. Words that appear in many documents receive a lower IDF score, while words that appear in fewer documents receive a higher score. This helps reduce the importance of commonly used words that offer little contextual value.
Example: If the word “cat” appears in only 1 out of 10 documents, its IDF value will be high, indicating uniqueness. On the other hand, if a word like “the” appears in all 10 documents, its IDF will be low because it is widely used and not distinctive.
TF-IDF Score:
The TF-IDF score is calculated by multiplying the Term Frequency by the Inverse Document Frequency. This combined score highlights words that occur frequently in a document while remaining relatively rare across other documents. Such words are considered highly relevant and meaningful.

Example: If “cat” has both a high TF and a high IDF, its resulting TF-IDF score will be strong, signaling that it is a key term within that document.

TF-IDF:

Importance and Context:
TF-IDF plays a crucial role in identifying the most meaningful terms within a document by balancing two key factors: term frequency and inverse document frequency. Term frequency measures how often a word appears within a specific document, while inverse document frequency evaluates how rare that word is across a broader collection of documents. This dual evaluation ensures that commonly used but insignificant words do not dominate the analysis. Instead, TF-IDF highlights terms that truly define the subject and intent of the content. Because of this balance, it is far more effective than simple keyword counting. It helps uncover what makes a document distinct, relevant, and contextually rich, allowing analysts to understand the true focus of the content rather than surface-level repetition.

Use Case:
TF-IDF is extensively applied in search engines, information retrieval systems, and modern SEO strategies. Search engines rely on it to assess document relevance when responding to user queries. In SEO, TF-IDF assists in keyword extraction, topic modeling, and content evaluation. By understanding term importance, digital marketers and analysts can improve content relevance, ensure better alignment with search intent, and enhance overall content quality. It also supports smarter indexing and ranking decisions, making it a foundational technique in content-driven digital ecosystems.

Use Cases of TF-IDF

SEO (Search Engine Optimization):
In SEO, TF-IDF helps determine which keywords carry genuine relevance for a specific webpage. Instead of overusing popular terms, website owners can focus on contextually important keywords that strengthen topical authority. This approach improves search engine rankings while maintaining natural readability. It also reduces keyword stuffing risks and supports long-term organic growth.

Content Creation:
For content creators, TF-IDF acts as a guide to understanding what topics and terms matter most within an article. It highlights core concepts that deserve emphasis, helping writers produce focused, informative, and well-structured content. This results in articles that satisfy both readers and search engines.Competitor Analysis:
TF-IDF is a powerful tool for analyzing competitor content. By comparing keyword importance across competing pages, website owners can identify missing topics, underused terms, and keyword opportunities. This insight enables smarter content planning, stronger differentiation, and improved competitive positioning.

#Step 1: Install Required Libraries

!pip install requests beautifulsoup4

Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.32.3)

Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (4.12.3)

Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)

Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)

Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)

Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.7.4)

Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4) (2.5)

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import TfidfVectorizer

import re

url = “https://thatware.co/”

response= requests.get(url)

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

Why response.text instead of response.content

response.text is used because:

Automatic Decoding:

It automatically converts the response content based on the character set defined in the response headers. This functionality is especially helpful when working with HTML data, as it ensures the text is properly decoded before being processed by BeautifulSoup. By handling decoding at this stage, it minimizes the risk of malformed or misinterpreted text caused by incorrect character representation.

Simplicity:

Since response.text returns content as a Unicode string, it can be directly passed to BeautifulSoup without additional preparation. BeautifulSoup is designed to work efficiently with Unicode strings, making this method straightforward and reliable. Using this approach also helps prevent common character encoding problems that may occur when encoding is incorrectly detected or manually managed during parsing.

Encoding:

The response.text attribute automatically decodes the response body by reading the encoding information provided in the HTTP headers. It relies on the charset defined in the Content-Type header, such as UTF-8 or ISO-8859-1. This automatic handling removes the need for manual decoding and ensures consistent text representation.

Use Case:

response.text is ideal when you need the HTML source as a readable string and when the website clearly defines its encoding in the response headers. It works best in scenarios where the server follows standard encoding practices.
Here, response.text proves valuable because it delivers HTML content as an already decoded string. BeautifulSoup can then efficiently transform this content into a structured, navigable parse tree for further analysis.

# Parse the HTML content using BeautifulSoup

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

soup

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<li class=”menu-item menu-item-type- menu-item-object- menu-item-has-children menu-parent-item menu-item-15025 normal-menu” id=”menu-item-15025″><a><span>OUR COMPANY</span></a><i class=”fa sub-mark fa-angle-down” tabindex=”0″></i>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19282″ id=”menu-item-19282″><a href=”https://thatware.co/about-us/“><span>ABOUT</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19407″ id=”menu-item-19407″><a href=”https://thatware.co/how-it-works/“><span>HOW IT WORKS</span></a></li>

<li class=”menu-item menu-item-type-custom menu-item-object-custom menu-item-19158″ id=”menu-item-19158″><a href=”https://thatware.co/basecamp/“><span>HOW WE MANAGE</span></a></li>

<li class=”menu-item menu-item-type-post_type menu-item-object-page menu-item-19414″ id=”menu-item-19414″><a href=”https://thatware.co/career/“><span>CAREER</span></a></li>

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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>

</ul>

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<!– #access –>

</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>

</input></div>

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</p>

<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>

</p>

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</form>

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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>

</div> <div class=”foot-items”>

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The Purpose of decompose() for Script and Style Elements

When scraping a webpage, the raw HTML often contains many elements that are unrelated to the core textual content. Along with visible text such as headings, paragraphs, and links, pages usually include <script>, <style>, and similar tags. These elements hold JavaScript logic, CSS styling rules, or technical instructions for browsers. While essential for rendering and interactivity, they add no value to text-based analysis. If left untouched, they introduce clutter and distort the results of content extraction. Removing them helps ensure that the scraped data reflects the actual information users read.

Identifying Script and Style Elements:
soup([‘script’, ‘style’])
This part of the process scans the parsed HTML and locates all <script> and <style> tags. Script tags commonly contain JavaScript code responsible for dynamic behavior, tracking, or third-party integrations. Style tags include CSS rules that define layout, fonts, colors, and spacing. Neither contributes meaningful language content. Identifying these elements is a necessary first step toward isolating human-readable text from technical markup.

Removing Script and Style Elements:
script_or_style.decompose()
The decompose() method completely removes the selected elements from the BeautifulSoup object. Unlike methods that simply hide or ignore content, decompose() deletes the tag and its contents from memory. After this operation, the soup object no longer contains any script or style data. This permanent removal ensures that subsequent text extraction steps work only with relevant HTML elements, resulting in a cleaner and more accurate dataset.

Extracting Clean Text:
text = soup.get_text()
Once script and style elements are removed, get_text() extracts all remaining textual content from the HTML. At this stage, the output consists primarily of visible text, such as headings, paragraphs, navigation labels, and anchor text. Because the underlying noise has already been eliminated, the extracted text better represents what users actually see on the webpage.

Cleaner Text Extraction:
By eliminating script and style tags, the extracted text focuses exclusively on meaningful content. This includes readable sections like articles, product descriptions, menus, and informational blocks. The result is text that aligns closely with user intent and page context. Cleaner extraction improves downstream processing, whether the goal is indexing, summarization, keyword extraction, or semantic analysis.Avoiding Noise:
If script and style elements are not removed, the extracted text may contain long strings of JavaScript functions or CSS rules. These irrelevant blocks can overwhelm real content and negatively impact text analysis tasks. For example, they can distort frequency counts in a Bag of Words (BoW) model or skew TF-IDF calculations by introducing technical terms unrelated to the topic. Removing this noise ensures more accurate models, clearer insights, and higher-quality analytical outcomes.

# 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()

text

text = re.sub(r’\s+’, ‘ ‘, text).strip()

1. re.sub(r’\s+’, ‘ ‘, text)

Purpose:
This line of code standardizes spacing within a text string by converting multiple consecutive whitespace characters into a single space. Whitespace can include spaces, tabs, and line breaks, which often appear inconsistently in raw or scraped text. By normalizing these variations, the text becomes cleaner, more readable, and easier to process for further analysis or display.

Components:

re.sub(pattern, replacement, string):
This function comes from Python’s re module, which handles regular expressions. It scans the input string for all matches of a specified pattern and replaces each match with the provided replacement value. The substitution occurs throughout the entire string, not just the first occurrence.

r’\s+’ (Pattern):

\s represents any whitespace character, including spaces, tabs, carriage returns, and newlines.
The + symbol is a quantifier meaning “one or more.”
Combined, \s+ matches any sequence of one or more consecutive whitespace characters, regardless of their type.

‘ ‘ (Replacement):
This is a single space character. Every matched sequence of whitespace is replaced with exactly one space, ensuring uniform spacing across the text.

text (String):
This is the original input string that needs to be cleaned. It may contain irregular spacing due to formatting, user input, or extracted data sources such as HTML or PDFs.

Example:
Input Text: “This is a\n\nsample text.”
Operation: Applying re.sub(r’\s+’, ‘ ‘, text) collapses all repeated whitespace into a single space.
Output: “This is a sample text.”

2. .strip()

.strip():
This is a built-in Python string method that removes whitespace characters from both the beginning and the end of a string. Unlike the previous step, it does not alter whitespace found within the body of the text.

Purpose:
Leading and trailing spaces are common issues when handling user input or extracted text. These extra characters may not be visible, but they can cause problems during comparisons, storage, or display. The .strip() method ensures that the string starts and ends cleanly.

Example:
Input Text: ” This is a sample text. “
Operation: Applying .strip() removes the unnecessary spaces at both ends.
Output: “This is a sample text.”

3. Putting It All Together

When combined, these two operations form a simple yet effective text-cleaning workflow. First, re.sub(r’\s+’, ‘ ‘, text) normalizes the internal spacing by replacing all sequences of multiple whitespace characters with a single space. This step ensures consistency throughout the text, regardless of how messy the original formatting was.

Next, .strip() removes any leftover whitespace from the beginning and end of the string. This final polish ensures the text is neatly trimmed and ready for use.

Together, these steps produce clean, readable, and standardized text. This approach is especially useful in data preprocessing, natural language processing, web scraping, and content normalization tasks where consistent formatting is essential.

Full Example:-

import re

Original text with extra spaces and newlines

text = ” This is a\n\nsample text. “

Clean the text by removing extra spaces and newlines

text = re.sub(r’\s+’, ‘ ‘, text).strip()

Output the cleaned text

print(text)

Output:

“This is a sample text.”

Explanation of Why This is Important:

Improves Readability: The cleaned text is more readable and consistent, making it easier to analyze or display.
Prepares Text for Further Processing: By removing unnecessary spaces and newlines, the text is in a cleaner format, which is essential for tasks like natural language processing (NLP), text analysis, or when creating models like Bag of Words (BoW) or TF-IDF.

# Clean the text by removing extra spaces and newlines

text = re.sub(r’\s+’, ‘ ‘, text).strip()

text

tfidf_vectorizer = TfidfVectorizer(stop_words=’english’)

Purpose:

This line initializes a TfidfVectorizer object from the sklearn (scikit-learn) library. The TfidfVectorizer is used to convert a collection of raw text documents into a matrix of TF-IDF features, which helps in identifying the importance of each word in the document relative to other words.

Components:

TfidfVectorizer: This is a tool that computes the Term Frequency-Inverse Document Frequency (TF-IDF) for each word in the text. TF-IDF is a statistical measure that evaluates the importance of a word in a document relative to a collection of documents (or corpus).
stop_words=’english’: This parameter tells the vectorizer to ignore common English stop words (e.g., “the,” “is,” “and”). Stop words are words that appear frequently but carry little meaningful information, so they are often removed in text analysis.

Example:

Let’s say you have a document with the text: “The quick brown fox jumps over the lazy dog.”
After removing stop words like “the” and “over,” the remaining words are “quick brown fox jumps lazy dog”.

# Initialize the TF-IDF Vectorizer

tfidf_vectorizer = TfidfVectorizer(stop_words=‘english’)

text_data = [text]

The Text is Treated as a Single Document Inside a List

Purpose:
This step ensures that the extracted text is correctly structured for processing by the TF-IDF algorithm. Although the content represents a single document, TfidfVectorizer is designed to work with a collection of documents rather than a standalone string. By placing the text inside a list, the code aligns the input format with the expectations of the vectorizer, allowing it to function without errors and maintain consistency in document handling.

Components:
text:
This variable contains the cleaned and preprocessed text obtained from the webpage. At this stage, unnecessary whitespace, formatting issues, and encoding inconsistencies have already been resolved, making the text ready for analysis.

text_data = [text]:
This line wraps the text variable inside a list, creating a collection with a single document. Even though only one document is being analyzed, this structure is essential because TfidfVectorizer requires an iterable input, such as a list or array, where each element represents a separate document.

By using this approach, the code remains flexible and scalable. Additional documents can be added to the list later without changing the overall logic. This simple yet important step ensures compatibility with the TF-IDF workflow and supports accurate keyword extraction and text analysis.

Example:

Suppose text = “quick brown fox jumps lazy dog”.
Then text_data = [“quick brown fox jumps lazy dog”].

# The text is treated as a single document inside a list

text_data = [text]

text_data

# Compute the TF-IDF matrix (matrix of TF-IDF scores for words in the text)

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

tfidf_matrix

1×773 sparse matrix of type ‘<class ‘numpy.float64′>’

with 773 stored elements in Compressed Sparse Row format>

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

Purpose:
This line of code is responsible for generating the TF-IDF matrix from the provided text data. The TF-IDF matrix captures how important each word is within the document by assigning a numerical weight based on term frequency and inverse document frequency. These weights reflect the relevance of words in the document relative to the overall text set. The fit_transform method performs this process in a single step by first analyzing the text to understand its vocabulary and statistical properties, and then converting the raw text into a structured, numerical representation. This matrix becomes the foundation for keyword extraction, relevance scoring, and further text analysis.

Components:

fit_transform(text_data):
This method combines two essential operations into one streamlined process, making it both efficient and convenient for text vectorization.

fit:
During the fitting stage, the model scans through the text data to identify all unique words and build a vocabulary. At the same time, it calculates the Inverse Document Frequency (IDF) for each term. IDF measures how rare or common a word is across the document set. Words that appear frequently across many documents receive lower importance, while more distinctive terms receive higher weights. Even when working with a single document, this step establishes the statistical baseline needed for TF-IDF computation.
transform:
Once the vocabulary and IDF values are established, the transform step converts the original text into a TF-IDF matrix. Each word in the vocabulary is assigned a TF-IDF score based on how often it appears in the document and how informative it is. This transformation turns unstructured text into a numerical format that can be easily analyzed, compared, or used in machine learning models.

tfidf_matrix:
This variable stores the resulting TF-IDF matrix produced by the transformation process. Each row in the matrix represents a document, and each column represents a term from the learned vocabulary. In this specific case, there is only one row because only one document is being processed. The values within the matrix indicate the relative importance of each word, enabling precise keyword identification and deeper content analysis.

Example:

Let’s assume text = “The quick brown fox jumps over the lazy dog.”.
After stop words are removed, you are left with “quick brown fox jumps lazy dog”
The TF-IDF matrix might look something like this (hypothetical scores):

The matrix shows that all three words have the same TF-IDF score since they each appear once in a single document, and there are no other documents to compare with (hence IDF doesn’t change their weight).

1. Get the List of Feature Names (Words in the Document)

keyboard_arrow_down

feature_names = tfidf_vectorizer.get_feature_names_out()

Purpose:

This line retrieves the list of words (features) that were identified by the TfidfVectorizer during the fitting process.

Components:

get_feature_names_out(): This method returns an array of the words (features) that were identified from the text data.

Example:

Given the text “quick brown fox jumps lazy dog”, the feature_names array might look like this:
feature_names: [‘brown’, ‘dog’, ‘fox’, ‘jumps’, ‘lazy’, ‘quick’]

2. Create a Dictionary of Words and Their Corresponding TF-IDF Scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

Purpose:

This line creates a dictionary that maps each word (from feature_names) to its corresponding TF-IDF score (from tfidf_matrix). This allows you to easily see the importance of each word in the document.

Components:

{} (Dictionary Comprehension): A Python technique to create a dictionary in a concise way.
feature_names[i]: Refers to the word at index i in the feature_names array.
tfidf_matrix[0, i]: Retrieves the TF-IDF score for the word at index i in the document (first row of the matrix).
for i in range(len(feature_names)): Loops through each index in the feature_names array.

Example:

Given feature_names = [‘brown’, ‘dog’, ‘fox’, ‘jumps’, ‘lazy’, ‘quick’] and the corresponding TF-IDF scores from the matrix, the dictionary might look like this :

tfidf_scores = {

‘brown’: 0.447,

‘dog’: 0.447,

‘fox’: 0.447,

‘jumps’: 0.447,

‘lazy’: 0.447,

‘quick’: 0.447

}

# Get the list of feature names (words in the document)

feature_names = tfidf_vectorizer.get_feature_names_out()

# Create a dictionary of words and their corresponding TF-IDF scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

tfidf_scores

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import TfidfVectorizer

import re

def scrape_website(url):

# Send a request to the website and get the content

response = requests.get(url)

# Check if the request was successful

if response.status_code != 200:

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

return “”

# Parse the website content using BeautifulSoup

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

# Extract text content from the website by removing script and style elements

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

script_or_style.decompose()

# Get the text content and clean it

text = soup.get_text()

text = re.sub(r’\s+’, ‘ ‘, text) # Remove extra whitespace

return text

def calculate_tfidf(text):

# Initialize the TF-IDF Vectorizer

tfidf_vectorizer = TfidfVectorizer(stop_words=’english’)

# Transform the text into a list (it expects a list of documents)

text_data = [text]

# Compute the TF-IDF matrix

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

# Get the feature names (words)

feature_names = tfidf_vectorizer.get_feature_names_out()

# Convert TF-IDF matrix to a dictionary of words and their scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

return tfidf_scores

def main():

# Example URL – you can replace this with any website URL

url = “https://thatware.co/”

# Scrape the website

text = scrape_website(url)

# Check if text was retrieved

if not text:

print(“No text found on the website.”)

return

# Calculate TF-IDF scores

tfidf_scores = calculate_tfidf(text)

# Sort and print the top 50 keywords by TF-IDF score

sorted_keywords = sorted(tfidf_scores.items(), key=lambda x: x[1], reverse=True)

print(“Top 50 keywords by TF-IDF score:”)

for keyword, score in sorted_keywords[:50]:

print(f”{keyword}: {score:.4f}”)

if __name__ == “__main__”:

main()

Below is a Python script that allows you to input multiple website URLs, scrape their content, and then compute the TF-IDF scores for each site. The code includes detailed comments to help a non-tech person understand each step.

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import TfidfVectorizer

import re

def scrape_website(url):

“””

This function takes a website URL as input, sends a request to the site,

and extracts the main text content by removing unnecessary HTML tags, scripts, and styles.

“””

try:

# Send an HTTP request to the website

response = requests.get(url)

# Check if the request was successful (status code 200 means success)

if response.status_code != 200:

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

return “”

# Parse the HTML content using BeautifulSoup

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 the text by removing extra spaces and newlines

text = re.sub(r’\s+’, ‘ ‘, text).strip()

return text

except Exception as e:

print(f”An error occurred while scraping {url}: {e}”)

return “”

def calculate_tfidf(text):

“””

This function takes the cleaned text content from a website as input and calculates the TF-IDF scores.

It returns a dictionary where each word is associated with its TF-IDF score.

“””

# Initialize the TF-IDF Vectorizer

tfidf_vectorizer = TfidfVectorizer(stop_words=’english’)

# The text is treated as a single document inside a list

text_data = [text]

# Compute the TF-IDF matrix (matrix of TF-IDF scores for words in the text)

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

# Get the list of feature names (words in the document)

feature_names = tfidf_vectorizer.get_feature_names_out()

# Create a dictionary of words and their corresponding TF-IDF scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

return tfidf_scores

def analyze_website_content(url):

“””

This function scrapes the content of the given website URL, calculates the TF-IDF scores,

and then prints the top keywords based on their scores.

“””

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

# Scrape the website content

text = scrape_website(url)

# If no text was retrieved, skip to the next website

if not text:

print(“No text found on the website.”)

return

# Calculate TF-IDF scores for the website’s text content

tfidf_scores = calculate_tfidf(text)

# Sort the keywords by their TF-IDF score in descending order

sorted_keywords = sorted(tfidf_scores.items(), key=lambda x: x[1], reverse=True)

# Print the top 20 keywords by TF-IDF score

print(“Top 20 keywords by TF-IDF score:”)

for keyword, score in sorted_keywords[:20]:

print(f”{keyword}: {score:.4f}”)

def main():

“””

The main function where we define multiple website URLs, analyze each one,

and print the TF-IDF results for each site.

“””

# 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

]

# Loop through each website URL in the list

for site in websites:

# Analyze the content and print the results

analyze_website_content(site)

# Print a separator for better readability between different website results

print(“\n” + “=”*50 + “\n”)

if __name__ == “__main__”:

main()

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

Top 20 keywords by TF-IDF score:

seo: 0.8051

services: 0.3432

ai: 0.2442

advanced: 0.1452

google: 0.1056

thatware: 0.0858

marketing: 0.0792

data: 0.0594

algorithms: 0.0528

based: 0.0528

building: 0.0528

content: 0.0528

development: 0.0528

link: 0.0528

search: 0.0528

algorithm: 0.0462

business: 0.0462

optimization: 0.0462

strategies: 0.0462

company: 0.0396

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

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

Top 20 keywords by TF-IDF score:

ðºð: 0.3324

000: 0.2908

free: 0.2701

âµ: 0.2285

²à: 0.2077

¾à: 0.2077

articles: 0.1662

èª: 0.1662

wikipedia: 0.1454

ªà: 0.1454

ð¹: 0.1454

bahasa: 0.1246

english: 0.1039

à²: 0.1039

ð²ð: 0.1039

ðºñ: 0.1039

ð½ñ: 0.1039

basa: 0.0831

ina: 0.0831

lã: 0.0831

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

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

Top 20 keywords by TF-IDF score:

hrs: 0.6677

bbc: 0.1781

agous: 0.1335

days: 0.1335

mins: 0.1335

trump: 0.1187

says: 0.1039

attack: 0.0890

chat: 0.0890

iran: 0.0890

tech: 0.0890

12: 0.0742

claims: 0.0742

investigated: 0.0742

musk: 0.0742

pm: 0.0742

uk: 0.0742

ukraine: 0.0742

agobbc: 0.0594

agomiddle: 0.0594

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

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

Top 20 keywords by TF-IDF score:

min: 0.3911

new: 0.3802

york: 0.3476

news: 0.1847

stories: 0.1521

times: 0.1195

access: 0.1086

included: 0.1086

learn: 0.1086

people: 0.1086

subscription: 0.1086

trump: 0.1086

world: 0.1086

biggest: 0.0978

ideas: 0.0869

loke: 0.0869

recommendations: 0.0869

day: 0.0760

tech: 0.0760

timesatul: 0.0760

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

1. Understanding the Importance of TF-IDF and Keywords
“The TF-IDF evaluation offers a clear understanding of which keywords your website content prioritizes. These terms are essential because they reflect your content’s core focus and signal to search engines what your site is most relevant for.”
Example:
“For example, in the review of your website (ThatWare), the keyword ‘SEO’ shows a notably high TF-IDF score, indicating strong emphasis. This implies that search engines are likely to associate your website closely with SEO-focused topics.”

2. Aligning Content with SEO Strategy
“By identifying keywords with the strongest TF-IDF values, you can confirm whether your content supports your intended SEO goals. If important keywords appear weak or absent, it clearly indicates the need for content refinement.”
Example:
“If your objective is to rank for AI-driven marketing strategies, but the term ‘AI’ reflects a lower TF-IDF score than expected, this signals a need to expand AI-focused content. This may include publishing new blogs, case studies, or dedicated pages targeting ‘AI’ as a priority keyword.”

3. Comparing with Competitors and Finding Opportunities
“TF-IDF insights also allow you to benchmark your content against competitors. By spotting keywords they prioritize that you currently overlook, you can uncover growth opportunities and differentiate your brand.”
Example:
“If a competitor strongly emphasizes ‘algorithm optimization’ with high TF-IDF values and your site does not, creating content around this topic can help attract traffic they currently capture.”

4. Impact on Search Engine Rankings and Traffic
“Content optimization guided by TF-IDF data can directly influence search rankings. Targeting valuable, relevant, and less-saturated keywords enhances visibility and drives higher organic traffic.”
Example:
“If ‘SEO services’ scores high in TF-IDF but delivers limited traffic, it may require better on-page optimization or stronger backlinks. Refining such pages can significantly improve rankings.”

5. Steps to Compete in the SEO Market
“To remain competitive in SEO, continuous content evaluation and optimization based on keyword data is essential.”
Steps:

1. Regularly Monitor TF-IDF Scores:
“Track keyword performance consistently to understand which terms are strengthening and which require improvement.”
Example: “Monitor ‘AI’ and ‘SEO services’ monthly to assess ongoing relevance.”

2. Content Optimization:
“Revise content to strengthen high-value keywords and create new material for underrepresented terms.”
Example: “Develop content around ‘data-driven marketing’ to capture emerging demand.”

3. Competitor Analysis:
“Compare TF-IDF metrics with competitors to identify keyword gaps and competitive advantages.”
Example: “If rivals rank for ‘link building strategies’ but lack ‘content development,’ combine both topics.”

4. Adjust and Iterate:
“SEO evolves constantly, requiring regular strategic updates.”
Example: “If long-form content gains priority, expand articles and integrate high-TF-IDF terms naturally.”

6. How These Insights Will Help Increase Traffic and Rankings
“Focusing on relevant keywords aligned with audience intent and search algorithms drives qualified traffic, stronger engagement, and higher conversions.”
Example:
“If ‘advanced SEO services’ performs poorly despite high TF-IDF, improving meta tags, UX, and internal linking can enhance results.”

“Optimizing your landing page for ‘advanced SEO services’ ensures better alignment with search engines and users. Strong meta tags improve visibility, improved UX boosts engagement, and internal links distribute authority. Together, these actions increase rankings, traffic, and conversions.”

Conclusion:
“Using TF-IDF insights strategically allows you to refine content, outperform competitors, and strengthen search visibility. This data-driven approach attracts quality traffic and positions your brand as an industry authority.”

Below is the rewritten code that not only calculates the TF-IDF scores for each keyword but also represents these keywords through a bar graph. The graph will display the top 20 keywords by their TF-IDF scores, with the size of each bar corresponding to the keyword’s importance.

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import TfidfVectorizer

import re

import matplotlib.pyplot as plt

def scrape_website(url):

“””

This function takes a website URL as input, sends a request to the site,

and extracts the main text content by removing unnecessary HTML tags, scripts, and styles.

“””

try:

# Send an HTTP request to the website

response = requests.get(url)

# Check if the request was successful (status code 200 means success)

if response.status_code != 200:

print(f“Failed to retrieve the content from {url}“)

return “”

# Parse the HTML content using BeautifulSoup

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 the text by removing extra spaces and newlines

text = re.sub(r‘\s+’, ‘ ‘, text).strip()

return text

except Exception as e:

print(f“An error occurred while scraping {url}: {e}“)

return “”

def calculate_tfidf(text):

“””

This function takes the cleaned text content from a website as input and calculates the TF-IDF scores.

It returns a dictionary where each word is associated with its TF-IDF score.

“””

# Initialize the TF-IDF Vectorizer with stop words removed

tfidf_vectorizer = TfidfVectorizer(stop_words=‘english’)

# The text is treated as a single document inside a list

text_data = [text]

# Compute the TF-IDF matrix (matrix of TF-IDF scores for words in the text)

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

# Get the list of feature names (words in the document)

feature_names = tfidf_vectorizer.get_feature_names_out()

# Create a dictionary of words and their corresponding TF-IDF scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

return tfidf_scores

def plot_tfidf_graph(tfidf_scores, url):

“””

This function plots a bar graph for the top 20 TF-IDF scores.

The larger the TF-IDF score, the larger the bar representing the keyword.

“””

# Sort the keywords by their TF-IDF score in descending order

sorted_keywords = sorted(tfidf_scores.items(), key=lambda x: x[1], reverse=True)

# Select the top 20 keywords

top_keywords = sorted_keywords[:20]

# Separate the keywords and their scores for plotting

keywords, scores = zip(*top_keywords)

# Plotting the bar graph

plt.figure(figsize=(10, 6))

plt.barh(keywords, scores, color=‘skyblue’)

plt.xlabel(‘TF-IDF Score’)

plt.title(f‘Top 20 Keywords by TF-IDF Score for {url}‘)

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

plt.show()

def analyze_website_content(url):

“””

This function scrapes the content of the given website URL, calculates the TF-IDF scores,

and then plots a graph of the top keywords based on their scores.

“””

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

# Scrape the website content

text = scrape_website(url)

# If no text was retrieved, skip to the next website

if not text:

print(“No content found on the website.”)

return

# Calculate TF-IDF scores for the website’s text content

tfidf_scores = calculate_tfidf(text)

# Plot the graph of the top keywords by TF-IDF score

plot_tfidf_graph(tfidf_scores, url)

def main():

“””

The main function where we define multiple website URLs, analyze each one,

and display the TF-IDF results graphically.

“””

# Example URLs for Testing

websites = [

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

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

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

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

]

# Loop through each website URL in the list

for site in websites:

# Analyze the content and plot the results

analyze_website_content(site)

# Print a separator for better readability between different website results

print(“\n” + “=”*50 + “\n”)

if __name__ == “__main__”:

main()

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

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

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

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

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

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

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

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

import requests

from bs4 import BeautifulSoup

from sklearn.feature_extraction.text import TfidfVectorizer

import re

import matplotlib.pyplot as plt

from wordcloud import WordCloud

def scrape_website(url):

“””

This function takes a website URL as input, sends a request to the site,

and extracts the main text content by removing unnecessary HTML tags, scripts, and styles.

“””

try:

# Send an HTTP request to the website

response = requests.get(url)

# Check if the request was successful (status code 200 means success)

if response.status_code != 200:

print(f“Failed to retrieve the content from {url}“)

return “”

# Parse the HTML content using BeautifulSoup

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 the text by removing extra spaces and newlines

text = re.sub(r‘\s+’, ‘ ‘, text).strip()

return text

except Exception as e:

print(f“An error occurred while scraping {url}: {e}“)

return “”

def calculate_tfidf(text):

“””

This function takes the cleaned text content from a website as input and calculates the TF-IDF scores.

It returns a dictionary where each word is associated with its TF-IDF score.

“””

# Initialize the TF-IDF Vectorizer with stop words removed

tfidf_vectorizer = TfidfVectorizer(stop_words=‘english’)

# The text is treated as a single document inside a list

text_data = [text]

# Compute the TF-IDF matrix (matrix of TF-IDF scores for words in the text)

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

# Get the list of feature names (words in the document)

feature_names = tfidf_vectorizer.get_feature_names_out()

# Create a dictionary of words and their corresponding TF-IDF scores

tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in range(len(feature_names))}

return tfidf_scores

def generate_wordcloud(tfidf_scores, url):

“””

This function generates a word cloud from the TF-IDF scores.

The larger the TF-IDF score, the larger the word in the word cloud.

“””

# Generate the word cloud from the TF-IDF scores

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

# Plotting the word cloud

plt.figure(figsize=(10, 6))

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

plt.axis(‘off’) # Remove the axes

plt.title(f‘Top Keywords by TF-IDF Score for {url}‘)

plt.show()

def analyze_website_content(url):

“””

This function scrapes the content of the given website URL, calculates the TF-IDF scores,

and then generates a word cloud of the top keywords based on their scores.

“””

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

# Scrape the website content

text = scrape_website(url)

# If no text was retrieved, skip to the next website

if not text:

print(“No content found on the website.”)

return

# Calculate TF-IDF scores for the website’s text content

tfidf_scores = calculate_tfidf(text)

# Generate the word cloud of the top keywords by TF-IDF score

generate_wordcloud(tfidf_scores, url)

def main():

“””

The main function where we define multiple website URLs, analyze each one,

and display the TF-IDF results as a word cloud.

“””

# Example URLs for Testing

websites = [

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

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

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

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

]

# Loop through each website URL in the list

for site in websites:

# Analyze the content and generate the word cloud

analyze_website_content(site)

# Print a separator for better readability between different website results

print(“\n” + “=”*50 + “\n”)

if __name__ == “__main__”:

main()

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

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

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

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

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

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

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

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

Hiring ThatWare provides businesses with strategic expertise, affordable pricing, extensive experience in digital marketing, and access to a full team capable of implementing successful search marketing campaigns. The company’s adaptability and commitment to experimentation are key advantages for businesses seeking effective SEO solutions.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker.