End-to-end unsupervised text analytics pipeline processing 390K+ #MeToo tweets with Python, including regex-based text normalization, vectorization, topic modeling (LDA/NMF), sentiment scoring (TextBlob), and temporal pattern mining. Provides interpretable topic clusters and visualization of discourse trajectories during the movement.

Social media provides a unique opportunity to shed light on phenomena that have previously been ignored or swept under the rug. The #MeToo movement began in 2006 when Tarana Burke first coined the phrase, but a viral Twitter post in 2017 served to lift the hashtag into the mainstream accompanied by the public reckoning of several high-profile me…

MeToo: Sentiment Analysis using Neural Networks. IEEE BigMM 2020 (2020)

This repo analyzes 390K+ #MeToo tweets using NLP to perform sentiment analysis and evaluate the movement’s global impact and key figure involvement.