Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization

Understanding users’ sentiment expression in social media is important in many domains, such as marketing and online applications. Is one demographic group inherently different from another? Does a group express the same sentiment both in private and public? How can we compare the sentiments of different groups composed of multiple attributes? In this paper, we take an interdisciplinary approach toward mining the patterns of textual sentiments and metadata. First, we look into several existing hypotheses in social science on the interplay between user characteristics and sentiments, as well as the related evidence in the field of social network data analysis. Second, we present a dataset with unique features (Facebook users chats and posts in multiple languages) and a procedure to process the data. Third, we test our hypotheses on this dataset and interpret the results. Fourth, under the subgroup discovery paradigm, we present an approach with two algorithms that generalizes single-attribute testing. This approach provides more detailed insight into the relationships among attributes and reveals interesting attribute value combinations with distinct sentiments. It also offers novel hypotheses for examination in future studies. Fifth, because the number of mined subgroup comparisons can be large, we develop an exploratory visualization tool that summarizes the comparisons and highlights meta-patterns.