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SemEval-2013: Sentiment Analysis in Twitter

Sentence-Level Sentiment Analysis

Descriptions

One model to learn them all (both language model and sentiment analysis -> sentiment2vec) alt text

Version

  • se-v1.ipynb: using sum all LSTM output vectors to predict sentiment label.
  • se-v2.ipynb: using sum all LSTM output vectors of sentiment word to predict sentiment label.
  • se-v3.ipynb: using sentiment embedding instead of one-hot vector.

Data & Result

Prerequisites

  • tensorflow 1.2
  • ipython notebook

Author

Binh Do