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In the competitive landscape of search engine optimization (SEO), leveraging advanced analytical techniques can significantly enhance your keyword targeting strategy. One such technique is the Bag of Words (BoW) model, a foundational concept in natural language processing that simplifies text into a collection of individual words or tokens, while disregarding grammar and word order. This model transforms text into quantifiable data, allowing for in-depth analysis of word frequency and relevance. By applying the Bag of Words model to SEO, you can uncover valuable insights into keyword usage, content gaps, and competitive positioning, enabling you to refine your content strategy and improve your search engine rankings with precision.
Objective and Purpose of the Project
The objective of this project is to provide an in-depth analysis of website content by identifying and highlighting significant keywords. This analysis is designed to help website owners optimize their content for enhanced search engine rankings, driving increased traffic and boosting overall SEO performance. The primary purpose of this initiative is to scrutinize the textual content of websites to pinpoint the most frequently used and impactful keywords. By doing so, our advanced tool from ThatWare enables website owners to assess which keywords are being emphasized and determine if these align with their broader SEO strategy. This valuable insight facilitates targeted content optimization, ultimately improving search engine visibility and attracting more relevant traffic.
Bag of Words (BoW) Concept
Understanding the Basics
The Bag of Words (BoW) model is a fundamental technique in natural language processing (NLP) and text analysis. It operates on the principle of representing text data in a simplified manner by breaking it down into individual words or tokens. Unlike traditional methods that consider the grammatical structure and order of words, BoW focuses solely on the frequency of each word’s occurrence. This approach converts text into a set of word counts, allowing for quantitative analysis.
Key Features of BoW
Tokenization: The first step in the BoW model is tokenization, where text is split into individual words or phrases. This process breaks down sentences into manageable units for analysis.
Frequency Count: Once tokenized, the BoW model counts the frequency of each word across the text. This count provides insights into which terms are most prevalent and potentially significant.
Vector Representation: The model then represents the text as vectors based on these word frequencies. Each document is converted into a numerical format that can be analyzed and compared.
Analyzing a Website for SEO
Identifying Key Content Areas
To effectively analyze a website for SEO, start by identifying key content areas where optimization can make a significant impact. Focus on core components such as blog posts, product descriptions, and landing pages. Each of these sections should be evaluated for keyword relevance and density, ensuring that they align with your target SEO strategy.
Conducting Keyword Frequency Analysis
Using tools and techniques like the Bag of Words (BoW) model, perform a keyword frequency analysis on the website’s content. This involves breaking down the text into individual words, counting their occurrences, and identifying the most frequently used keywords. This analysis helps reveal which terms are being emphasized and whether they align with your SEO objectives.
Evaluating Keyword Alignment
Once key keywords are identified, assess their alignment with your overall SEO strategy. Check if these keywords match your target audience’s search intent and if they are appropriately distributed throughout the content. Ensure that high-value keywords are used strategically in titles, headings, and body text to maximize visibility.
Optimizing for Better Results
Based on the analysis, refine and optimize your content. Incorporate relevant keywords more effectively, address content gaps, and improve keyword density where necessary. Regularly monitor and update your content to keep it aligned with evolving SEO trends and maintain strong search engine rankings.
For instance, if a cooking website frequently features terms such as “recipe,” “cook,” and “ingredients,” it indicates that the site is effectively targeting those keywords. Conversely, if these keywords are underrepresented, the site owner might need to incorporate additional content focused on recipes to better attract and engage visitors.
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# Import Necessary libraries.
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
[ ]
url= ‘https://thatware.co/’
# Send an HTTP request to the website
response = requests.get(url)
# Parse the website’s HTML content
soup = BeautifulSoup(response.content, ‘html.parser’)
soup
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<li><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>
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<!–<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>
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<!–<p class=’seo-ts’>SEO</p>
<p class=’ds-ts’>DATA <br>SCIENCE</p>
<p class=’ai-ts’>AI</p>
<p class=’ml-ts’>MACHINE <br>LANGUAGE</p>–>
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<h5>Pioneer the Fusion of SEO with Artificial Intelligence and Data Science – a World-First Innovation!</h5>
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<h6>Revolutionary SEO: Infusing AI for Unmatched Results – Your Professional SEO Partner!</h6>
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<h6>Recognized and Awarded Worldwide by 200+ Elite Media Houses, Including:
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<li class=”heart”>Forbes Select 200</li>
<li class=”heart”>Clutch</li>
<li class=”heart”>TedX</li>
<li class=”heart”>BrightonSEO</li>
<li class=”heart”>GoodFirms</li>
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<li class=”heart”>Manifest</li>
<li class=”heart”>STEVIE’S</li>
<li class=”heart”>Inventiva</li>
<!–<li class=”heart”>ECONOMIC TIMES</li>–>
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<h2>GET A CUSTOMIZED SEO AUDIT & DIGITAL MARKETING STRATEGY FOR YOUR BUSINESS. START BY SHARING YOUR DETAILS BELOW!</h2>
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<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>
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<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>
<a class=”shutter-btn” href=”https://thatware.co/why-ai/“>Read More</a>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
<a class=”shutter-btn” href=”https://thatware.co/advanced-seo-services/“>Read More</a>
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<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>
<a class=”shutter-btn” href=”https://thatware.co/digital-marketing-services/“>Read More</a>
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<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>
<a class=”shutter-btn” href=”https://thatware.co/link-building-services/“>Read More</a>
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<!– <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>–>
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<!– <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>–>
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<!– <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>–>
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<!– <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>–>
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<p><iframe allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” allowfullscreen=”” data-lazy-src=”https://www.youtube.com/embed/jzyVVCzySlU?feature=oembed” data-rocket-lazyload=”fitvidscompatible” frameborder=”0″ height=”180″ loading=”lazy” src=”about:blank” title=”Cost of Retrieval | What is it? Semantic SEO | Brighton SEO” width=”320″></iframe><noscript><iframe allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” allowfullscreen=”” frameborder=”0″ height=”180″ src=”https://www.youtube.com/embed/jzyVVCzySlU?feature=oembed” title=”Cost of Retrieval | What is it? Semantic SEO | Brighton SEO” width=”320″></iframe></noscript></p>
<p>Brighton</p>
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<p><iframe allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” allowfullscreen=”” data-lazy-src=”https://www.youtube.com/embed/09JNSZfIFwc?feature=oembed” data-rocket-lazyload=”fitvidscompatible” frameborder=”0″ height=”180″ loading=”lazy” src=”about:blank” title=”Power of Adapting to the Digital Transformation Ecosystem | Dr. Tuhin Banik | TEDxAnandNagar” width=”320″></iframe><noscript><iframe allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” allowfullscreen=”” frameborder=”0″ height=”180″ src=”https://www.youtube.com/embed/09JNSZfIFwc?feature=oembed” title=”Power of Adapting to the Digital Transformation Ecosystem | Dr. Tuhin Banik | TEDxAnandNagar” width=”320″></iframe></noscript></p>
<p>Tedx</p>
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<p>Forbes</p>
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<h2>CASE STUDIES</h2>
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<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>
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<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>
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<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>
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<a href=”https://thatware.co/starter-seo-services/“>One Time 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/>
<a href=”https://thatware.co/nlp-services/“>NLP and AI </a><br/>
<a href=”https://thatware.co/online-reputation-management/“>ORM</a></p>
<p style=”font-size: 13px; font-weight: 800; color: white;”>Work Flow’s</p>
<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>
<p><img alt=”” class=”aligncenter size-thumbnail wp-image-21911″ data-lazy-src=”https://thatware.co/wp-content/uploads/2024/07/Welby-Consulting-Top-Digital-Marketing-Company-GoodFirms-2019-150×150-1.webp” decoding=”async” height=”150″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20150%20150’%3E%3C/svg%3E” width=”150″><noscript><img alt=”” class=”aligncenter size-thumbnail wp-image-21911″ decoding=”async” height=”150″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2024/07/Welby-Consulting-Top-Digital-Marketing-Company-GoodFirms-2019-150×150-1.webp” width=”150″/></noscript></img></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/cbd-seo-services/“>SEO for CBD</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/pets-seo-services/“>SEO for Pets</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://theceo.in/industry/thatware-an-innovator-of-digital-marketing-pushing-limits-with-artificial-intelligence/“>The CEO</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://inc42.com/resources/ow-artificial-intelligence-is-redefining-the-face-of-ecommerce-marketing/“>Inc 42</a><br/>
<a href=”https://www.livemint.com/brand-post/thatware-innovates-digital-marketing-with-ai-and-deep-learning-modules-1558002426519.html“>Live Mint</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>
<p><img alt=”Google Certified” class=”aligncenter size-large wp-image-21913″ data-lazy-src=”https://thatware.co/wp-content/uploads/2024/07/google-certified-badge-1024×351-1.webp” decoding=”async” height=”274″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20800%20274’%3E%3C/svg%3E” width=”800″><noscript><img alt=”Google Certified” class=”aligncenter size-large wp-image-21913″ decoding=”async” height=”274″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2024/07/google-certified-badge-1024×351-1.webp” width=”800″/></noscript></img></p>
<p><img alt=”Google Partner” class=”aligncenter size-large wp-image-21912″ data-lazy-src=”https://thatware.co/wp-content/uploads/2024/07/february-19-2019-blog-1024×536-1.webp” decoding=”async” height=”419″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20800%20419’%3E%3C/svg%3E” width=”800″><noscript><img alt=”Google Partner” class=”aligncenter size-large wp-image-21912″ decoding=”async” height=”419″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2024/07/february-19-2019-blog-1024×536-1.webp” width=”800″/></noscript></img></p>
<p> </p>
<p><img alt=”Moz Certified” class=”aligncenter size-thumbnail wp-image-21916″ data-lazy-src=”https://thatware.co/wp-content/uploads/2024/07/60185feb9b28c8.46766414-150×150-1.webp” decoding=”async” height=”150″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20150%20150’%3E%3C/svg%3E” width=”150″><noscript><img alt=”Moz Certified” class=”aligncenter size-thumbnail wp-image-21916″ decoding=”async” height=”150″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2024/07/60185feb9b28c8.46766414-150×150-1.webp” width=”150″/></noscript></img></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>
<p><a href=”https://thatware.co/onpage-seo-trends/“>DIY on-page SEO </a><br/>
<a href=”https://thatware.co/offpage-seo-trends/“>DIY off-page SEO</a><br/>
<a href=”https://thatware.co/google-my-business/“>DIY GMB Guide</a><br/>
<a href=”https://thatware.co/technical-seo/“>DIY Tech SEO</a></p>
<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 style=”font-size: 13px; font-weight: 800; color: white;”>Our AI-SEO Tool</p>
<p><a href=”https://www.webtool.co/“>WebTool</a></p>
<p><img alt=”Hubspot certified” class=”aligncenter size-full wp-image-21919″ data-lazy-src=”https://thatware.co/wp-content/uploads/2024/07/HubSpot-Platinum-Partner-Badge-copy.webp” decoding=”async” height=”366″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20500%20366’%3E%3C/svg%3E” width=”500″><noscript><img alt=”Hubspot certified” class=”aligncenter size-full wp-image-21919″ decoding=”async” height=”366″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2024/07/HubSpot-Platinum-Partner-Badge-copy.webp” width=”500″/></noscript></img></p>
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</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>
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<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-phone”><i class=”fa fa-phone”></i><a href=”tel:+91-7044080698″>+91-7044080698</a></div>
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<div class=”contact-email”><i class=”fa fa-envelope”></i><a href=”info@thatware.co”>info@thatware.co</a></div>
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<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>
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<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>
<p><img alt=”thatware iso certificate” class=”aligncenter size-full wp-image-21910″ data-lazy-src=”https://thatware.co/wp-content/uploads/2022/10/ThatWare-ISO.webp” decoding=”async” height=”200″ src=”data:image/svg+xml,%3Csvg%20xmlns=’http://www.w3.org/2000/svg’%20viewBox=’0%200%20200%20200’%3E%3C/svg%3E” width=”200″><noscript><img alt=”thatware iso certificate” class=”aligncenter size-full wp-image-21910″ decoding=”async” height=”200″ loading=”lazy” src=”https://thatware.co/wp-content/uploads/2022/10/ThatWare-ISO.webp” width=”200″/></noscript></img></p>
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<!– This website is like a Rocket, isn’t it? Performance optimized by WP Rocket. Learn more: https://wp-rocket.me – Debug: cached@1723156911 –>
get_text(): This is a method provided by BeautifulSoup that extracts all the text from the HTML content stored in the soup variable
[ ]
# Extract the text from the webpage.
text = soup.get_text()
text
text.split(): This splits the text into a list of words, using any amount of whitespace (spaces, tabs, newlines) as the delimiter.
text=” Welcome to my website \n\nThis is a paragraph of text. “
text.split()= [“Welcome”, “to”, “my”, “website”, “This”, “is”, “a”, “paragraph”, “of”, “text.”]
‘ ‘.join(…): This joins the list of words back into a single string, using a single space character as the separator.
Then, the join() method will join the list of words back into a single string, using a single space character as the separator:
clean_text = ‘ ‘.join(text.split())
clean_text = “Welcome to my website This is a paragraph of text.”
[ ]
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
clean_text
CountVectorizer: A Tool to Convert Text to Numbers.
What is CountVectorizer?
- Imagine you have a tool that can read text and count how often each word appears. CountVectorizer is that tool in Python.
- It converts text into a format that computers can understand—specifically, it turns text into a list of word counts.
What Are Stop Words?
- Stop words are common words like “the”, “and”, “is”, which usually don’t carry significant meaning in a sentence.
- By setting stop_words=’english’, we tell the tool to ignore these common words, so we focus on the more important words.
Example
- Suppose you have a sentence: “The cat sits on the mat.”
- Without removing stop words, “the” would be counted, but it doesn’t add much meaning. By removing stop words, we focus on words like “cat”, “sits”, and “mat”.
Fitting and Transforming Text into a Bag of Words Model
X = vectorizer.fit_transform([text])
- Fit and Transform—What Does It Mean?
- “Fitting” means that the tool learns from the text you provide. It identifies all the unique words in the text.
- “Transforming” means converting the text into numbers based on what was learned. Specifically, it counts how many times each word appears.
Example:
- If your text is “Cat mat cat mat cat”, the tool will learn that there are two unique words: “cat” and “mat”.
- Then it will transform the text into numbers, resulting in something like: {“cat”: 3, “mat”: 2}.
[ ]
# Use CountVectorizer to create the BoW model, removing stop words
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
X
<1×773 sparse matrix of type ‘<class ‘numpy.int64′>’
with 773 stored elements in Compressed Sparse Row format>
Converting the Bag of Words Model to a DataFrame
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
Introduction to DataFrames
In data analysis, a DataFrame is a powerful data structure provided by libraries like Pandas in Python. It allows for easy manipulation, analysis, and visualization of data in a tabular format. Converting the Bag of Words (BoW) model into a DataFrame makes it easier to handle and analyze text data, providing a structured view of word frequencies and their distribution.
Transforming BoW Output
To convert the Bag of Words model output into a DataFrame, follow these steps:
Create the BoW Model:
Begin by applying the Bag of Words model to your text data. This involves tokenizing the text and counting the frequency of each word. Use libraries like Scikit-learn to generate a matrix of word counts, where rows represent documents and columns represent words.
Generate the DataFrame:
Once you have the word count matrix, import the Pandas library and create a DataFrame. Each row of the DataFrame corresponds to a document, and each column represents a specific word from the BoW model. The cell values are the frequencies of the words in the corresponding documents.
Labeling and Indexing:
Label the DataFrame columns with the unique words identified by the BoW model and set the index to represent different documents or text samples. This labeling helps in easily identifying and accessing specific keywords and their frequencies.
Analyzing the DataFrame:
With the DataFrame created, you can perform various analyses, such as sorting words by frequency, identifying the most common keywords, and visualizing word distributions. This structured format allows for efficient data manipulation and insightful analysis.
Example:
Consider the text “apple banana apple orange”. After processing, the resulting DataFrame could appear as follows:
This table indicates that “apple” occurs 2 times, “banana” 1 time, and “orange” 1 time.
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
- X.toarray()
- What It Does: Converts the BoW matrix into a 2D NumPy array.
- Why It’s Needed: The BoW model created by CountVectorizer is in a sparse matrix format, which is efficient for storing data but not very readable. toarray() converts it into a regular 2D array where each row represents a document, and each column represents a word.
Example:
- Suppose you have the following simple text document:
“apple banana apple”- The BoW model might be represented as a sparse matrix like this:
[[2, 1]]
- The BoW model might be represented as a sparse matrix like this:
- 2 is the count for “apple” and 1 is the count for “banana”.
- After toarray(), it becomes:
[[2, 1]] - vectorizer.get_feature_names_out()
- What It Does: Retrieves the list of words (features) that the CountVectorizer used.
- Why It’s Needed: This list of words is used to label the columns of the DataFrame. Each column corresponds to one of these words.
- Example:
If your vocabulary from the text document was [‘apple’, ‘banana’], get_feature_names_out() will return:
[‘apple’, ‘banana’]
- pd.DataFrame(…)
- What It Does: Creates a DataFrame using the 2D array and the list of feature names.
- Why It’s Needed: A DataFrame is a tabular format that’s easier to read and manipulate compared to raw arrays. Each column represents a word, and each row represents a document (though here we have just one document).
- Example:
- With the 2D array [[2, 1]] and the feature names [‘apple’, ‘banana’], the DataFrame will look like this:
[ ]
# Convert the BoW model into a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
bow_df
bow_df.sum(axis=0)
Understanding axis=0
The axis parameter in pandas determines the direction along which a function like sum() operates.
- axis=0: This means you want to sum down the columns. So, for each column (which represents a word), you add up all the values in that column.
- axis=1: This would sum across the rows. In this case, it would add up all the word counts in each row (which is not what we want here).
Why Use sum(axis=0)?
In the BoW (Bag of Words) model, each column of the DataFrame represents the count of a particular word. Since we’re interested in knowing how often each word appears in the entire document (or across multiple documents if there were more rows), we need to add up the counts in each column.
Let’s say you have a DataFrame that looks like this:
If you call bow_df.sum(axis=0), you are summing each column’s values:
- For the “apple” column: The sum is 2.
- For the “banana” column: The sum is 1.
- For the “orange” column: The sum is 1.
So, the result of sum(axis=0) would be:
This tells you that “apple” appears 2 times, “banana” 1 time, and “orange” 1 time in the text. The axis=0 is crucial here because it sums down the columns to give you the total count of each word across the entire text.
Summary
- axis=0: Adds up values in each column (which represents each word) to get the total frequency of that word across the entire text.
- This is important because it helps us find out which words are most common in the text, which can then be used to analyze keyword usage for SEO purposes.
[ ]
# Sort words by frequency in descending order
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
word_frequencies
[ ]
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
# Step 1: Extract Text Content from a Website
def get_website_text(url):
try:
# Send an HTTP request to the website
response = requests.get(url)
# Parse the website’s HTML content
soup = BeautifulSoup(response.content, ‘html.parser’)
# Extract the text from the webpage
text = soup.get_text()
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
return clean_text
except Exception as e:
print(f”Failed to retrieve content from {url}: {e}”)
return None
# Step 2: Create the BoW Model without Stop Words
def create_bow_model(text):
# Use CountVectorizer to create the BoW model, removing stop words
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
# Convert the BoW model into a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
return bow_df
# Step 3: Analyze Word Frequencies
def analyze_word_frequencies(bow_df):
# Sort words by frequency in descending order
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
# Print the most common keywords (excluding stop words)
print(“Most Common Keywords:\n”)
print(word_frequencies.head(50)) # Top 50 most frequent meaningful words
return word_frequencies
# Step 4: Main Function to Run the Analysis
def analyze_website_content(url):
print(f”Analyzing content from: {url}\n”)
# This line calls the get_website_text(url) function to extract all the text from the specified website.
text_content = get_website_text(url)
if text_content: # This line checks if the text_content variable is not empty or None. This ensures that text was successfully retrieved from the website.
bow_df = create_bow_model(text_content) # This line calls the create_bow_model(text_content) function to convert the text into a Bag of Words (BoW) model.
analyze_word_frequencies(bow_df) #This line calls the analyze_word_frequencies(bow_df) function to find out which words are the most common in the text.
else:
print(“No content to analyze.”)
# Website URL for Analysis
website_url = ‘https://thatware.co/’ # ThatWare homepage
# Analyze the specified website
analyze_website_content(website_url)
Analyzing content from: https://thatware.co/
Most Common Keywords:
seo 122
services 52
ai 37
advanced 22
google 16
thatware 13
marketing 12
data 9
link 8
algorithms 8
content 8
development 8
building 8
based 8
search 8
algorithm 7
optimization 7
business 7
strategies 7
company 6
online 6
intelligence 6
page 6
media 6
digital 5
case 5
clients 5
science 5
strategy 5
best 5
read 5
diy 5
website 5
faq 5
time 4
agency 4
edge 4
nlp 4
artificial 4
provide 4
backlink 4
design 4
research 4
web 4
keyword 4
2024 4
site 4
india 4
success 4
contact 4
dtype: int64
[ ]
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
# Step 1: Extract Text Content from a Website
def get_website_text(url):
try:
response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser’)
# Extract text from the webpage
text = soup.get_text()
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
return clean_text
except Exception as e:
print(f”Failed to retrieve content from {url}: {e}”)
return None
# Step 2: Create the BoW Model without Stop Words
def create_bow_model(text):
# Use CountVectorizer with stop words removed
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
# Convert the BoW model to a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
return bow_df
# Step 3: Analyze Word Frequencies
def analyze_word_frequencies(bow_df):
# Sort words by frequency in descending order
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
# Print the most common words (excluding stop words)
print(“Most Common Keywords:\n”)
print(word_frequencies.head(20)) # Top 10 most frequent meaningful words
return word_frequencies
# Step 4: Main Function to Run the Analysis
def analyze_website_content(url):
print(f”Analyzing content from: {url}\n”)
text_content = get_website_text(url)
if text_content:
bow_df = create_bow_model(text_content)
analyze_word_frequencies(bow_df)
else:
print(“No content to analyze.”)
# Example URLs for Testing
websites = [‘https://thatware.co/’, # ThatWare homepage
‘https://www.seotechexperts.com/seo-agency-india.html’, # seotechexperts homepage
‘https://www.techwebers.com/’, # Techwebers homepage
‘https://www.incrementors.com/’ # incrementors homepage
]
# Test the code on example websites
for site in websites:
analyze_website_content(site)
print(“\n” + “=”*50 + “\n”)
Analyzing content from: https://thatware.co/
Most Common Keywords:
seo 122
services 52
ai 37
advanced 22
google 16
thatware 13
marketing 12
data 9
link 8
algorithms 8
content 8
development 8
building 8
based 8
search 8
algorithm 7
optimization 7
business 7
strategies 7
company 6
dtype: int64
==================================================
Analyzing content from: https://www.seotechexperts.com/seo-agency-india.html
Most Common Keywords:
seo 60
services 23
brand 14
provide 14
make 12
marketing 11
business 11
clients 10
experts 10
strategies 9
result 8
agency 7
industry 7
strategy 7
page 7
report 7
technical 7
project 7
professionals 7
time 6
dtype: int64
==================================================
Analyzing content from: https://www.techwebers.com/
Most Common Keywords:
seo 22
website 21
online 14
webers 12
tech 12
digital 10
help 8
divi 7
search 7
create 7
design 7
team 6
presence 6
marketing 6
web 6
engine 5
world 5
results 5
services 4
visibility 4
dtype: int64
==================================================
Analyzing content from: https://www.incrementors.com/
Most Common Keywords:
marketing 23
business 18
free 15
traffic 13
website 13
best 11
sales 11
services 10
strategy 10
digital 7
online 7
incrementors 7
customers 7
30 7
results 6
target 6
highly 6
minute 6
seo 6
team 6
dtype: int64
==================================================
1. Understanding the Content Focus
What It Means: The Bag of Words (BoW) analysis reveals the most frequently used keywords on a website, providing insight into the primary focus of the content.
Example: Analyzing the content from https://thatware.co/ shows that keywords like “SEO,” “services,” “AI,” and “advanced” appear most often. This suggests that the site emphasizes SEO services, AI technologies, and advanced strategies.
How It Helps:
Alignment with Business Goals: By examining if these keywords align with the site’s core offerings, the owner can verify whether the content matches their business focus. For example, if “SEO” and “AI” are primary services, the content is appropriately aligned. However, if the aim is to highlight different services, adjustments may be needed.
Attracting the Right Audience: Ensuring that the content features keywords relevant to what potential customers are searching for enhances the chances of attracting the right traffic.
2. Identifying Content Gaps or Overemphasis
What It Means: BoW analysis can pinpoint keywords that are either overly prominent or lacking in the content.
Example: If “SEO” appears 122 times while “digital marketing” only appears 5 times, this may indicate an overemphasis on SEO at the expense of other important services.
How It Helps:
Content Optimization: The website owner can balance keyword usage by increasing focus on underrepresented terms like “digital marketing” if it’s a key service. This adjustment helps create a more comprehensive content strategy.
Improving Keyword Density: The analysis aids in fine-tuning keyword density to avoid overuse (keyword stuffing) or underuse, ensuring that important terms are neither excessively repeated nor neglected.
3. Adjusting Content for Better User Engagement
What It Means: The BoW analysis can indicate whether the content effectively engages the target audience.
Example: Frequent keywords such as “strategy,” “best,” and “success” suggest that the content is geared towards strategic advice or success stories.
How It Helps:
Content Relevance: Adjusting content to align with audience interests ensures it remains relevant. For instance, if the objective is to generate more business inquiries, the content might need more actionable insights or strong calls to action.
User Retention: Content that effectively uses the right keywords can engage visitors longer, decreasing bounce rates and improving overall user retention.
[ ]
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
# Step 1: Extract Text Content from a Website
def get_website_text(url):
try:
response = requests.get(url)
# Parse the HTML content using BeautifulSoup with response.text
soup = BeautifulSoup(response.text, ‘html.parser’)
# Remove script and style elements to clean the content
for script_or_style in soup([‘script’, ‘style’]):
script_or_style.decompose()
# Extract the text content from the HTML
text = soup.get_text()
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
return clean_text
except Exception as e:
print(f”Failed to retrieve content from {url}: {e}”)
return None
# Step 2: Create the BoW Model without Stop Words
def create_bow_model(text):
# Use CountVectorizer with stop words removed
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
# Convert the BoW model to a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
return bow_df
# Step 3: Analyze Word Frequencies
def analyze_word_frequencies(bow_df):
# Sort words by frequency in descending order
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
# Print the most common words (excluding stop words)
print(“Most Common Keywords:\n”)
print(word_frequencies.head(20)) # Top 20 most frequent meaningful words
return word_frequencies
# Step 4: Main Function to Run the Analysis
def analyze_website_content(url):
print(f”Analyzing content from: {url}\n”)
text_content = get_website_text(url)
if text_content:
bow_df = create_bow_model(text_content)
analyze_word_frequencies(bow_df)
else:
print(“No content to analyze.”)
# Example URLs for Testing
websites = [‘https://thatware.co/’, # ThatWare homepage
‘https://www.wikipedia.org/’, # Wikipedia homepage
‘https://www.bbc.com/’, # BBC News homepage
‘https://www.nytimes.com/’ # New York Times homepage
]
# Test the code on example websites
for site in websites:
analyze_website_content(site)
print(“\n” + “=”*50 + “\n”)
Analyzing content from: https://thatware.co/
Most Common Keywords:
seo 122
services 52
ai 37
advanced 22
google 16
thatware 13
marketing 12
data 9
link 8
algorithms 8
content 8
development 8
building 8
based 8
search 8
algorithm 7
optimization 7
business 7
strategies 7
company 6
dtype: int64
==================================================
Analyzing content from: https://www.wikipedia.org/
Most Common Keywords:
ðºð 16
000 14
free 13
âµ 11
²à 10
¾à 10
èª 8
articles 8
ªà 7
ð¹ 7
wikipedia 7
bahasa 6
ð½ñ 5
english 5
ð²ð 5
ಠ5
ðºñ 5
ina 4
ðµð 4
འ4
dtype: int64
==================================================
Analyzing content from: https://www.bbc.com/
Most Common Keywords:
hrs 38
days 16
says 10
mins 10
agous 9
olympics 9
paris 9
bbc 8
day 7
gender 6
olympic 6
town 5
medal 5
2024 5
ukraine 5
gold 5
family 5
week 5
new 4
trump 4
dtype: int64
==================================================
Analyzing content from: https://www.nytimes.com/
Most Common Keywords:
min 39
new 29
york 24
news 19
stories 14
world 11
included 10
learn 10
subscription 10
access 10
people 9
ideas 8
biggest 8
recommendations 8
day 7
make 7
tech 7
times 7
best 7
cooking 6
dtype: int64
==================================================
Explanation of Changes
Using response.text:
response.text retrieves the HTML content as a Unicode string, making it more convenient to process the text content of a webpage.
Removing <script> and <style> Elements:
The updated code now includes a loop that identifies and removes <script> and <style> elements using the decompose() method. This step ensures that JavaScript code and CSS styles, which are not part of the main content, are excluded from the text extraction process.
Text Extraction:
After cleaning the HTML content, the text is extracted using soup.get_text(), which provides a clearer focus on the actual content displayed on the webpage.
Why These Changes Are Useful:
- Cleaner Text Extraction: By eliminating unnecessary elements like <script> and <style>, the extracted text more accurately reflects what users see on the page. This is essential for precise analysis, as it avoids the inclusion of irrelevant code or styles in your word frequency analysis.
- Improved Analysis Results: With cleaner, more relevant text, the Bag of Words (BoW) model and subsequent word frequency analysis can concentrate on meaningful terms, enhancing the quality of insights derived from the content.
[ ]
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
# Step 1: Extract Text Content from a Website
def get_website_text(url):
try:
response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser’)
# Extract text from the webpage
text = soup.get_text()
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
return clean_text
except Exception as e:
print(f”Failed to retrieve content from {url}: {e}”)
return None
# Step 2: Create the BoW Model without Stop Words
def create_bow_model(text):
# Use CountVectorizer with stop words removed
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
# Convert the BoW model to a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
return bow_df
# Step 3: Generate Word Cloud from BoW Model
def generate_wordcloud(bow_df):
# Sum the occurrences of each word to get the total frequency
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
# Convert the word frequencies to a dictionary format
word_freq_dict = word_frequencies.to_dict()
# Generate the word cloud
wordcloud = WordCloud(width=800, height=400, background_color=’white’).generate_from_frequencies(word_freq_dict)
# Plot the word cloud
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation=’bilinear’)
plt.axis(‘off’) # Remove the axes
plt.show()
# Step 4: Main Function to Run the Analysis
def analyze_website_content(url):
print(f”Analyzing content from: {url}\n”)
text_content = get_website_text(url)
if text_content:
bow_df = create_bow_model(text_content)
generate_wordcloud(bow_df)
else:
print(“No content to analyze.”)
# Example URLs for Testing
websites = [
‘https://thatware.co/’, # ThatWare homepage
‘https://www.seotechexperts.com/seo-agency-india.html’, # SEO Tech Experts homepage
‘https://www.techwebers.com/’, # Techwebers homepage
‘https://www.incrementors.com/’ # Incrementors homepage
]
# Test the code on example websites
for site in websites:
analyze_website_content(site)
print(“\n” + “=”*50 + “\n”)
[ ]
import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import matplotlib.pyplot as plt
# Step 1: Extract Text Content from a Website
def get_website_text(url):
try:
response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser’)
# Extract text from the webpage
text = soup.get_text()
# Clean up the text by removing extra spaces, line breaks, etc.
clean_text = ‘ ‘.join(text.split())
return clean_text
except Exception as e:
print(f”Failed to retrieve content from {url}: {e}”)
return None
# Step 2: Create the BoW Model without Stop Words
def create_bow_model(text):
# Use CountVectorizer with stop words removed
vectorizer = CountVectorizer(stop_words=’english’)
# Fit and transform the text into a BoW model
X = vectorizer.fit_transform([text])
# Convert the BoW model to a DataFrame for easier analysis
bow_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
return bow_df
# Step 3: Generate Bar Graph from BoW Model
def plot_bow_graph(bow_df):
# Sum the occurrences of each word to get the total frequency
word_frequencies = bow_df.sum(axis=0).sort_values(ascending=False)
# Select the top 20 keywords
top_keywords = word_frequencies.head(20)
# Plotting the bar graph
plt.figure(figsize=(12, 8))
plt.barh(top_keywords.index, top_keywords.values, color=’skyblue’)
plt.xlabel(‘Frequency’)
plt.ylabel(‘Keywords’)
plt.title(‘Top 20 Keywords by Bag of Words Frequency’)
plt.gca().invert_yaxis() # Invert y-axis to show the highest frequencies at the top
plt.show()
# Step 4: Main Function to Run the Analysis
def analyze_website_content(url):
print(f”Analyzing content from: {url}\n”)
text_content = get_website_text(url)
if text_content:
bow_df = create_bow_model(text_content)
plot_bow_graph(bow_df)
else:
print(“No content to analyze.”)
# Example URLs for Testing
websites = [
‘https://thatware.co/’, # ThatWare homepage
‘https://www.seotechexperts.com/seo-agency-india.html’, # SEO Tech Experts homepage
‘https://www.techwebers.com/’, # Techwebers homepage
‘https://www.incrementors.com/’ # Incrementors homepage
]
# Test the code on example websites
for site in websites:
analyze_website_content(site)
print(“\n” + “=”*50 + “\n”)