2019/08/19 by Mohsen Farhadloo, Farhadloo, Mohsen
Computer Science · Social Sciences · #Computational and Text Analysis Methods #Electoral Systems and Political Participation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.1908.07061
openalex publication_date 2019/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.