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This project focuses on analyzing Twitter trends to uncover popular topics, discussions, and user sentiments. By examining hashtags, keywords, and user interactions, valuable insights are gained in real-time social media data, aiding in understanding public opinion and identifying influential users.
The NLPbrl wrapper API is a package for wrapping The Rosette Text Analytics API's functions. It can use natural language processing, and machine learning to analyze unstructured and semi-structured text in multilingual. It can provide a large number of powerful text analyses which has largely helped people to learn about natural language processing
This repository will guide you to understand basic operations and functions in Natural Language Processing using NLTK and also included small example on Sentimental Analysis.
Fouille des sujets abordée par les marocains dans les medias sociaux (Facebook et Twitter) et les sites d'actualités marocaines entre Janvier 2018 et Janvier 2020.
This project uses latent dirichlet allocation for topic modeling. It first generates the document term matrix. Then It builds the latent dirichlet allocation algorithm to extract latent topics in the documents. A wordcloud function is also implemented to display the representative words of each topics.