Twitter Sentiment on Affordable Care Act using Score Embedding

TL;DR

This paper introduces score embedding, a neural network-based method for learning interpretable word representations to analyze Twitter sentiment on the Affordable Care Act, revealing public opinion trends and sentiment dynamics.

Contribution

The paper presents a novel supervised score embedding technique that effectively captures sentiment information in word representations for health care discussions on Twitter.

Findings

Score embedding outperforms existing methods in sentiment analysis.

Negative sentiment towards 'TrumpCare' was consistently higher than neutral and positive.

The approach effectively incorporates sentiment information into word embeddings.

Abstract

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.