Newspaper article collection using Python and ML
Used topic modelling on a collection of 2693 newspaper articles to predict the top 10 topics in the articles. Cleaned the data using Lemmatization, tokenization and other pre-processing required.
Created on 11th May 2023
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Newspaper article collection using Python and ML
Used topic modelling on a collection of 2693 newspaper articles to predict the top 10 topics in the articles. Cleaned the data using Lemmatization, tokenization and other pre-processing required.
The problem Newspaper article collection using Python and ML solves
Newspaper_topic_modelling is a Python-based project for extracting hidden themes from news articles using natural language processing and topic modeling techniques. The project uses the Newspaper and Gensim libraries for data collection, text preprocessing, and topic modeling, while PyLDAVis is used for visualizing the results. The end result is a framework that enables users to gain deeper insights into current events and discover underlying patterns and themes that may not be immediately apparent from the news articles. With its user-friendly interface and powerful features, Newspaper_topic_modelling is a valuable tool for researchers, journalists, and anyone interested in understanding the news in a more meaningful way.
Challenges I ran into
The quality of the news articles collected can vary widely, and some articles may contain errors or inaccuracies that can affect the quality of the analysis.