Understanding users' sentiments 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 towards 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. Furthermore, it offers novel hypotheses for examination in future studies.
|Title of host publication||2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 25-28 August 2015, Paris, France|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||6|
|Publication status||Published - 2015|