In this project, I'll leverage Natural Language Processing (NLP) and the power of term frequency-inverse document frequency (TF-IDF) to analyze news articles. By identifying the most significant terms in each article, I can gain quick insights into their topics, making it easier for me to navigate the world of news. I'll work with a selection of articles from The News International, a prominent English-language newspaper in Pakistan. My goal is to use TF-IDF to examine each article's content and determine the terms that best describe its topic.
Here's how I'll approach this:
1. Importing Libraries: I'll start by importing essential libraries, including CountVectorizer, TfidfTransformer, and TfidfVectorizer from scikit-learn. These tools will help me perform TF-IDF analysis.
2. Text Inspection: I'll take a closer look at one of the provided articles to understand its content and structure.
3. Data Preprocessing: To prepare the articles for analysis, I'll preprocess them by tokenizing and lemmatizing the text. The preprocessed articles will be stored in a list called processed_articles.
4. Calculate TF-IDF Scores: I'll calculate TF-IDF scores using two different approaches:
- First, I'll start with word counts (Bag-of-Words) by initializing a CountVectorizer and fitting it on the processed articles. I'll convert the word counts into a DataFrame.
- Then, I'll convert these word counts into TF-IDF scores using a TfidfTransformer.
- Finally, I'll calculate TF-IDF scores directly using a TfidfVectorizer.
5. Verify Results: To ensure consistency, I'll compare the TF-IDF scores obtained from the two different approaches. If they match, I'll confirm that my analysis is on the right track.
6. Infer Word Importances: To gain insight into each article's topic, I'll identify the term with the highest TF-IDF score for each article. While this approach is simple, it provides a quick glimpse into the main themes of the articles.
By the end of this project, I'll have a better understanding of the topics covered in these news articles, making it easier for me to stay informed in a fast-paced world. Let's dive into the analysis and uncover the insights hidden within the articles!