This project explores sentiment analysis using Twitter data. It uses various text vectorization techniques (e.g.: one hot, TF-IDF) to represent tweets and several models to predict the sentiment of tweets (i.e.: positive or negative). The best performing model is a stacked LSTM architecture.Text pre-processing is multi-threaded to increase speed.
ekloberdanz/TwitterSentimentAnalysis
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Developed a sentiment analysis tool with the Twitter API. This tool scrapes twitter for recent tweets containing any chosen keyword, and applies the best pre-trained ML model to classify the sentiment of each tweet.
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