Characteristics of opinions in the societal and non-societal domains

Abstract

With the increasing availability of user opinions on the web, understanding the distinct nature of opinions in societal and non-societal contexts becomes crucial for opinion mining and sentiment analysis tasks. Societal topics, encompassing social unrest, terrorist acts, and government policies, differ significantly from non-societal topics like product reviews, movie reviews, and restaurant reviews. Given the regional specificity of societal issues and the lack of sentiment-annotated resources for them, this paper highlights the need to comprehend the differences in opinions between these domains for effective sentiment analysis. Through statistical text and network analysis, it investigates word usage, sentiment word association, and homogeneity in societal versus non-societal contexts. The study also explores graph-based analysis as a novel approach to sentiment analysis, considering its advantage in easily expanding context through the addition of nodes, as opposed to the complexity of inserting relevant tokens in text. The findings suggest that while non-societal sentiment resources might not be directly applicable to societal domains, graph-based analysis offers promising avenues for sentiment analysis in diverse societal topics.