Twitter Sentiment on Affordable Care Act using Score Embedding

Mohsen Farhadloo, PhD John Molson Scool of Business, Concordia University mohsen.farhadloo@concordia.ca August 21, 2019 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. 1 Introduction Sentiment analysis as a type of text categorization is the task of identifying the sentiment orientation of documents written in natural language which assigns one of the predefined sentiment categories into a whole document or pieces of the document such as phrases or sentences [23, 8]. Many studies used binary classification and reported high performance [18, 29, 24] and some studies have observed that the performance of the categorization reduces as the number of sentiment categories increases [2, 16, 3, 11]. Bag-Of-Words (BOW), a standard approach for text categorization, represents a document by a vector that indicates the words that appear in the document.