vahadruya/Capstone-Project-Unsupervised-ML-Topic-Modelling
The project explores a dataset of 2225 BBC News Articles and identifies the major themes and topics present in them. Topic Modeling algorithms such as Latent DIrichlet Allocation and Latent Semantic Analysis have been implemented. Effetiveness of the method of vectorization has also been explored
This project helps organizations and content managers categorize large collections of news articles by identifying their core themes. You input raw news article texts, and the project outputs a list of prominent topics within those articles, along with an assessment of how well the articles fit into these identified categories. This is ideal for content strategists, news editors, or anyone managing extensive text archives.
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Use this if you need to automatically sort or summarize a large volume of textual content like news articles to understand the main discussions.
Not ideal if you require highly nuanced sentiment analysis or a detailed deep-dive into specific entities mentioned within the texts.
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Aug 06, 2023
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