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In this paper we introduce score embedding, a neural network based model tolearn interpretable vector representations for words. Score embedding is asupervised method that takes advantage of the labeled training data and theneural network architecture to learn interpretable representations for words.Health care has been a controversial issue between political parties in theUnited States. In this paper we use the discussions on Twitter regardingdifferent issues of affordable care act to identify the public opinion aboutthe existing health care plans using the proposed score embedding. Our resultsindicate our approach effectively incorporates the sentiment information andoutperforms or is at least comparable to the state-of-the-art methods and thenegative sentiment towards "TrumpCare" was consistently greater than neutraland positive sentiment over time.

Twitter Sentiment on Affordable Care Act using Score Embedding