NMF-Based Topic Modeling for SEO and Engagement Insights

The primary purpose of this project, at its initial stage, is to help a website owner or content creator gain insights into the key topics or themes that are prevalent across the different pages of their website. By using Non-negative Matrix Factorization (NMF), we can automatically identify these hidden topics from the text of multiple webpages without needing to manually read through each page.

This process is useful for:

• Understanding the focus of the content across a website.
• Identifying common themes or topics that the website covers.
• Helping website owners align their content with their business or marketing goals by analyzing which topics are emphasized.

In this project, we used Non-negative Matrix Factorization (NMF) to analyze the text from multiple website pages. The purpose is to identify the key topics and themes across the website, giving the owner clear insights into what their content is focused on. By understanding these topics, the business can:

• Ensure their content aligns with their business goals.
• Adjust their content strategy based on the topics that are covered.
• Identify opportunities for new content creation or optimization.

What is Non-negative Matrix Factorization (NMF)?

Non-negative Matrix Factorization (NMF) is a technique used in data analysis and machine learning to break down a large matrix (table of numbers) into smaller pieces. The key feature of NMF is that it works only with non-negative numbers (numbers greater than or equal to zero). It’s used to simplify complex data into more manageable parts while preserving essential information. Think of it like breaking down a large document into a few main topics.

How Does NMF Work?

Imagine you have a large dataset, like a collection of documents, where each document can be represented by the frequency of words (or terms) that appear in it. NMF takes this data and tries to group it into patterns. It breaks down the data into two smaller matrices:

1. Basis matrix: This tells you the “topics” or patterns found in the data.
2. Coefficient matrix: This tells you how strongly each document (or piece of data) belongs to each topic.

Use Cases of NMF

• Topic Modeling: NMF is often used in topic modeling, which means discovering the main themes in a large collection of documents. For example, if you feed a collection of news articles into NMF, it might group the articles into topics like “sports,” “politics,” and “technology” based on the words in them.
• Content Analysis: In content analysis, NMF can help in breaking down huge amounts of unstructured data (like text) into meaningful parts, making it easier to understand or classify. For example, it can analyze customer reviews to find the most talked-about features of a product.
• Image Processing: NMF can be used to break down images into their component parts, helping with things like facial recognition by identifying different parts of a face (eyes, nose, etc.) based on pixel values.
• Music or Sound Processing: In audio processing, NMF can separate different sound sources in a recording (like separating vocals from instruments in a song).

Step 1: Import necessary libraries

Purpose:

• requests is a library that allows us to fetch data from the web. Specifically, it helps us download the content of web pages so that we can analyze them.

Use Case:

• Imagine you have a list of webpages, like those from a blog or service section of a website. requests allows us to pull the text content from those pages by sending a request to the webpage. This is similar to how a browser loads a page when you visit it, but in this case, we’re doing it programmatically to collect the data.

Purpose:

• TfidfVectorizer is a tool that helps us convert text into numbers. In this case, it’s transforming the words on a webpage into a format that the computer can understand for further analysis.

Use Case:

• When we analyze text, the computer can’t understand words like “SEO” or “traffic” directly. TfidfVectorizer turns these words into a matrix of numbers based on how frequently each word appears and how important it is relative to other words in the document. This allows us to use mathematical models (like NMF) on the text.
• Example: If a webpage frequently mentions “SEO” but only mentions “Google” once, TF-IDF will give “SEO” a higher value to show its importance.

Purpose:

• NMF (Non-negative Matrix Factorization) is the core model that we are using to perform topic modeling. This tool will discover the hidden topics in the text by breaking down the TF-IDF matrix (the numerical representation of the text) into smaller, understandable groups of words (topics).

Use Case:

• Let’s say you have content from 10 webpages, but you don’t want to read through all of them. NMF will analyze the text and tell you what topics are discussed on those pages by grouping related words. For example, it may tell you that one topic is about “SEO optimization” and another is about “content marketing” based on the most frequent and important words.

Purpose:

• BeautifulSoup is a library used to extract the text from HTML. Web pages are written in a format called HTML, which includes not only the visible text but also a lot of code (like headers, footers, and navigation menus). BeautifulSoup helps us strip away all the unnecessary code and focus only on the important text we want to analyze.

Use Case:

• When you open a webpage, you see text, images, and links, but behind that, there is HTML code that tells the browser how to display everything. BeautifulSoup helps us pull out just the text content (like paragraphs and headings) from the HTML, which we can then analyze using NMF. For example, if you’re analyzing blog content, BeautifulSoup will extract the actual blog post text, ignoring the page layout and formatting code.

Browse the full article here: https://thatware.co/nmf-based-topic-modeling-for-seo/