Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.
neural-network sklearn python3 twitter-streaming-api wordcloud nltk naive-bayes-classifier json-parser logistic-regression flask-web twitter-sentiment-analysis sastrawi-python indonesian-language trax wikimedia-data-dump gensim-word2vec softmax-layer relu-layer embedding-layer-keras
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Updated
Sep 13, 2020 - Jupyter Notebook