Twitter Sentiment Analysis using RNNs and fasttext pretrained word embeddings.
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Compare a BiGRU + MLP model with a Bi-LSTM + deep self-attention + MLP model
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Fine- tune the best of them
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Data Source: https://www.kaggle.com/kazanova/sentiment140
This is the sentiment140 dataset. It contains 1,600,000 tweets extracted using the twitter api . The tweets have been annotated (0 = negative, 4 = positive) and they can be used to detect sentiment.
Content
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It contains the following 6 fields:
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target: the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)
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ids: The id of the tweet ( 2087)
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date: the date of the tweet (Sat May 16 23:58:44 UTC 2009)
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flag: The query (lyx). If there is no query, then this value is NO_QUERY.
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user: the user that tweeted (robotickilldozr)
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text: the text of the tweet (Lyx is cool)