Topic modeling and Recommendation system using LDA And Cosine Similarity

In this study, we explore the application of Natural Language Processing (NLP) techniques to analyze and recommend articles from Medium. By utilizing a dataset of Medium articles, we employ topic modeling using Latent Dirichlet Allocation (LDA) and implement a recommendation system using Doc2Vec to suggest articles similar to a given piece. The methodology includes preprocessing text data, visualizing insights, building an LDA model to discover latent topics, and creating a content-based recommendation system. Our findings show the potential of these techniques in enhancing user engagement and content discovery on digital platforms.