Get Better Keyword Clarity With TF-IDF Powered SEO Extractor

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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 compared to other pages. This information is critical for optimizing content for search engines, improving SEO, and ultimately enhancing the website’s visibility and traffic.

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’s a method used to evaluate how important a word is in a particular document compared to all the other documents in a collection (or corpus). In simple terms, it’s a way to find out which words are most significant in a text by considering how often they appear and how rare they are across other texts.

Breaking Down TF-IDF

Term Frequency (TF):

This measures how frequently a word appears in a document. The more times a word appears, the higher its TF score.
Example: If the word “cat” appears 10 times in a 100-word article, the TF for “cat” would be 10/100 = 0.1.

Inverse Document Frequency (IDF):

This measures how unique or rare a word is across multiple documents. If a word appears in many documents, its IDF score is lower. If it appears in fewer documents, its IDF score is higher.
Example: If “cat” appears in 1 out of 10 documents, the IDF would be higher, showing that “cat” is relatively unique. If “the” appears in all 10 documents, its IDF would be lower because it’s common.

TF-IDF Score:

The TF-IDF score is the product of TF and IDF. It helps highlight words that are frequent in a document but not common in many documents, marking them as important keywords.
Example: If “cat” has a high TF and a high IDF, its TF-IDF score will be high, indicating it’s a significant term in that document.

TF-IDF:

Importance and Context: TF-IDF helps identify the most important words in a document, considering both their frequency within the document and their rarity across other documents. This makes it more powerful for identifying keywords and understanding what makes a document unique.
Use Case: TF-IDF is widely used in search engines, information retrieval, and SEO to rank documents based on their relevance to a query, extract keywords, and enhance the quality of content analysis.

Use Cases of TF-IDF

SEO (Search Engine Optimization):
- TF-IDF helps identify which keywords are most relevant to a specific content page. Website owners can optimize these keywords to improve their search engine rankings.
Content Creation:
- It helps content creators understand what words are important in their articles or pages, guiding them to focus on the right topics and terms.
Competitor Analysis:
- By analyzing competitors’ content using TF-IDF, website owners can identify gaps or opportunities to target specific keywords that competitors might have missed.

#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 decodes the content according to the charset specified in the response headers. This is useful for parsing HTML content, as it ensures the text is in the correct encoding before it’s passed to BeautifulSoup.
Simplicity: Since response.text gives a Unicode string, it’s ready for immediate use with BeautifulSoup, which works well with Unicode strings. This approach avoids potential issues with character encoding that could arise if the encoding isn’t correctly guessed or handled by BeautifulSoup.

Encoding: response.text automatically decodes the content of the response based on the response headers. It uses the charset specified in the Content-Type header (e.g., utf-8, iso-8859-1).

Use Case: You would use response.text when you want the HTML content as a string and when the website properly specifies the encoding in the headers.
Here, response.text is useful because it gives you the HTML content as a decoded string, which BeautifulSoup can then parse into a navigable tree structure.

# Parse the HTML content using BeautifulSoup

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

soup

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<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>

</ul>

</li>

<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>

</li>

</ul></div> </nav>

<!– #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>

</p>

</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>

</div>

<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>

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The Purpose of decompose() for script and style Elements

When you scrape a webpage, the HTML content often includes various elements such as <script>, <style>, and others that are not relevant to the main text content. These elements contain JavaScript code, CSS styles, or other content that you typically do not want in your text extraction because they can introduce unnecessary noise.

Identifying script and style Elements:

soup([‘script’, ‘style’]): This part of the code searches the HTML content for all <script> and <style> elements. These tags usually contain JavaScript code or CSS styling, which do not contribute to the main textual content of a webpage.

Removing script and style Elements:

script_or_style.decompose(): The decompose() method is used to remove these elements entirely from the BeautifulSoup object (soup). This means that after this operation, the soup object no longer contains any of the script or style content.

Extracting Clean Text:

text = soup.get_text(): After removing the <script> and <style> elements, this line extracts all the remaining text from the HTML, which is now cleaner and more representative of the actual content displayed on the webpage.

Cleaner Text Extraction: By removing the script and style elements, you ensure that the extracted text is focused on the content that is visible to users (e.g., paragraphs, headings, links) rather than behind-the-scenes code or styles. This leads to a more accurate and meaningful extraction of the website’s text.

Avoiding Noise: If you don’t remove these elements, the extracted text might include large blocks of JavaScript code or CSS styles, which are not useful for most text analysis tasks, such as creating a Bag of Words (BoW) model or calculating TF-IDF scores.

# 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 part of the code replaces multiple consecutive whitespace characters (such as spaces, tabs, and newlines) with a single space.

Components:

re.sub(pattern, replacement, string): This is a function from Python’s re module (regular expressions) that searches for all occurrences of a pattern in a string and replaces them with the specified replacement.

r’\s+’ (Pattern):
- \s is a regular expression pattern that matches any whitespace character (spaces, tabs, newlines, etc.).
- ” + is a quantifier that means “one or more occurrences.”
- Together, \s+ matches one or more consecutive whitespace characters.
‘ ‘ (Replacement): This is a single space character that will replace the matched whitespace sequence.
text (String): This is the input text that you want to clean.

Example:

Input Text: “This is a\n\nsample text.”
Operation: re.sub(r’\s+’, ‘ ‘, text) will replace all instances of multiple spaces and newlines with a single space.
Output: “This is a sample text.”

2. .strip()

.strip(): This is a string method in Python that removes any whitespace characters from the beginning and end of the string. It does not affect whitespace within the string.

Example:

Input Text: ” This is a sample text. “
*Operation: *.strip() will remove the spaces at the beginning and end of the string.
Output: “This is a sample text.”

3. Putting It All Together

The combined operation cleans the text by:

Replacing multiple consecutive whitespace characters (including spaces, newlines, tabs) within the text with a single space.
Removing any leading or trailing whitespace from the beginning and end of the text.

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 line places the text content into a list, treating it as a single document. TfidfVectorizer expects a list of documents, even if there’s only one document, so we need to put the text in a list.

Components:

text: This is the cleaned text that you’ve extracted from the webpage.
text_data = [text]: This creates a list containing a single element (the text). This is necessary because the TfidfVectorizer expects an iterable of documents.

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 computes the TF-IDF matrix for the text data. The matrix represents the TF-IDF scores of the words in the document. The fit_transform method first fits the model to the data (learning the vocabulary and the IDF values) and then transforms the text into a TF-IDF-weighted term-document matrix.

Components:

fit_transform(text_data): This method does two things:
1. fit: Learns the vocabulary from the text data and calculates the Inverse Document Frequency (IDF) for each term.
2. transform: Transforms the text data into a TF-IDF matrix, where each cell in the matrix represents the TF-IDF score of a word in the document.
tfidf_matrix: This is the output matrix containing the TF-IDF scores. Each row corresponds to a document (in this case, just one), and each column corresponds to a word in the vocabulary.

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 analysis provides a clear picture of which keywords your website content is currently emphasizing. These keywords are critical as they indicate what your content is most focused on, and by extension, what search engines might recognize your site for.”

Example:

“For instance, in the analysis of your website (ThatWare), the keyword ‘SEO’ has a very high TF-IDF score, indicating that this term is highly emphasized. This suggests that search engines may associate your site strongly with SEO-related content.”

2. Aligning Content with SEO Strategy

“By understanding which keywords have the highest TF-IDF scores, you can ensure that your website is aligned with your intended SEO strategy. If certain important keywords are missing or underrepresented, this is a clear sign that you need to adjust your content to better target those terms.”

Example:

“If your goal is to rank highly for AI-driven marketing strategies, but ‘AI’ has a lower TF-IDF score than expected, this indicates a need to incorporate more AI-focused content on your site. This could involve creating new blog posts, case studies, or pages that specifically target ‘AI’ as a key term.”

3. Comparing with Competitors and Finding Opportunities

“You can use these TF-IDF insights to compare your content against that of your competitors. By identifying which keywords they are ranking for that you may not be focusing on, you can find opportunities to fill those gaps and stand out in the market.”

Example:

“Suppose your competitor’s website emphasizes ‘algorithm optimization’ with high TF-IDF scores, but your site does not. You could create content around ‘algorithm optimization’ to capture traffic that your competitor is currently attracting, giving you an edge.”

4. Impact on Search Engine Rankings and Traffic

“Optimizing content based on TF-IDF scores can have a direct impact on search engine rankings. By focusing on high-value keywords that are both relevant and not overly saturated, you can improve your site’s visibility on search engines, leading to increased organic traffic.”

Example:

“For instance, if ‘SEO services’ has a high TF-IDF score on your site but isn’t driving the expected traffic, it may be worth exploring why—perhaps by improving on-page SEO factors or building backlinks to pages that emphasize this term. Adjusting your content to more effectively target these high-value keywords can help boost your rankings.”

5. Steps to Compete in the SEO Market

“To effectively compete in the SEO market, it’s crucial to regularly analyze and adjust your content based on the latest keyword data. Here’s how you can do it:”

Steps:

Regularly Monitor TF-IDF Scores:

“Continuously monitor the TF-IDF scores of your content. Identify which keywords are gaining traction and which are not performing as expected.”
Example: “Track ‘AI’ and ‘SEO services’ monthly to see if they continue to be strong keywords or if adjustments are needed.”

2. Content Optimization:

“Update your content to emphasize high-TF-IDF keywords that align with your business goals. Create new content to target keywords that are currently underrepresented.”
Example: “If ‘data-driven marketing’ is a growing trend, create blog posts or case studies that focus on this area to capture new search traffic.”

3. Competitor Analysis:

“Compare your TF-IDF results with those of your competitors. Identify areas where you can outperform them by targeting keywords they are missing or underutilizing.”
Example: “If a competitor ranks highly for ‘link building strategies’ but has low TF-IDF for ‘content development,’ you can create content that bridges these topics.”

4. Adjust and Iterate:

“SEO is an ongoing process. Continuously adjust your strategy based on new data, changes in search engine algorithms, and shifts in user behavior.”
Example: “If Google updates its algorithm to favor long-form content, consider expanding your articles and incorporating more high-TF-IDF keywords naturally throughout.”

6. How These Insights Will Help Increase Traffic and Rankings

“By focusing on the right keywords and ensuring your content is aligned with the needs of your audience and the algorithms of search engines, you can drive more qualified traffic to your site. This increased traffic will not only boost your rankings but also lead to higher engagement and conversions.”

Example:

“If ‘advanced SEO services’ has a high TF-IDF score but isn’t driving the expected traffic, you might need to optimize the landing page for this keyword further, perhaps by improving meta tags, enhancing the user experience, or building more internal links. These changes can help ensure that the keyword performs better, leading to an increase in traffic and a higher ranking.”
“By optimizing your landing page for the keyword ‘advanced SEO services,’ you can ensure that your content is better aligned with both search engine algorithms and user expectations. Enhancing your meta tags will improve how your page is represented in search results, while a better user experience will keep visitors engaged and encourage them to take action. Building more internal links will not only help users navigate your site but also boost the SEO value of your pages by distributing page authority. These combined efforts will lead to higher search engine rankings, increased traffic, and ultimately more conversions.”

Conclusion:

“By leveraging the insights from the TF-IDF analysis, you can fine-tune your content strategy, outperform competitors, and improve your site’s visibility in search engine results. This strategic approach will not only help you attract more traffic but also position your brand as a leader in your industry.”

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

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

Get Better Keyword Clarity With TF-IDF Powered Web Keyword Extractor – Next Gen SEO with Hyper Intelligence

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tfidf_vectorizer = TfidfVectorizer(stop_words=’english’)

text_data = [text]

tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)

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