A sentiment analysis framework for social issues
University of New Brunswick
Sentiment analysis investigates attitudes, feelings, and expressed opinions regarding products, services, topics, or issues. Subjectivity classification that categorizes text as objective or subjective is an application of sentiment analysis. Sentiment classification, as another application, categorizes the polarity of opinion mostly as positive or negative. This research focuses on the sentiment analysis of social issues. We have conducted a research that statistically shows that the affective factors on opinions in product domains are different from those in social domains. Based on the findings of this research, a framework is proposed for sentiment analysis of social issues. This framework considers the role of verb in sentiment and defines a quadruple structure for opinion that consists of opinion author, opinion target, opinion expression, and opinion time. One of benefits of the proposed framework is that it extracts expressed opinions that can be used for various applications such as subjectivity classification, sentiment polarity classification, sentiment summarization, sentiment visualization, and sentiment comparison. We have evaluated the performance of our proposed framework for sentiment analysis of public comments regarding abortion as a social issue. We have implemented two applications of sentiment analysis: subjectivity classification and polarity classification.