Extracting Themes and Sentiments in Birth Control Drug Reviews with DistilBert and LDA Topic Modeling
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Updated
Aug 19, 2024 - Jupyter Notebook
Extracting Themes and Sentiments in Birth Control Drug Reviews with DistilBert and LDA Topic Modeling
Scrape Steam/Metacritic reviews and perform LDA topic and sentiment analysis
This project includes Twitter Social Media Analytics, Facebook Graph Analysis and News API Data Analysis. See the final report for full understanding.
A Case Study On The Rising Omicron Cases and Public Sentiment Analysis using Twitter Data
Code Repository for the assignments required for the Natural Language Processing master's course at PSUT.
Topic modeling using Word2Vec and LDA algorithm with Python
Text clustering in spark with scala using LDA Model on a TF-IDF matrix
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.
Topic-modeling on large data (1.85M tweets written in Spanish, ~1M "Spain geolocated", about 'coronavirus' between 2019 to 2020-04-20). Forked from ShuaiW/twitter-analysis (adapted for Python3 to use a discriminative score), mainly for Twitter LDA (Latent Dirichlet allocation using Gibbs sampling, https://lda.readthedocs.io/)
Unsupervised machine learning exploration of NBA topics on Twitter.
Using the foundation and understanding of my first approach to song sentiment analysis using lyrics, created a more robust and better approach to this problem.
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