Twitter Sentiment on Affordable Care Act using Score Embedding
This work addresses sentiment analysis for health care policy discussions on social media, but it is incremental as it applies a novel method to a specific domain without broad foundational impact.
The paper tackled the problem of analyzing public sentiment on the Affordable Care Act from Twitter data by introducing score embedding, a neural network model for interpretable word representations, and found that negative sentiment towards 'TrumpCare' was consistently higher than neutral or positive sentiment over time.
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.