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