Fuzzy-Set Based Sentiment Analysis of Big Social Data

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

Abstract—Computational approaches to social media analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by the social philosophical approach of relational sociology. There are no other unified modelling approaches to social data that integrate the conceptual, formal, software, analytical and empirical realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on fuzzy set theory and describe the operational semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth, we use SODATO to fetch social data from the facebook wall of a global brand, H&M and conduct a sentiment classification of the posts and comments. Fifth, we analyse the sentiment classifications by constructing crisp as well as the fuzzy sets of the artefacts (posts, comments, likes, and shares). We document and discuss the longitudinal sentiment profiles of artefacts and actors on the facebook page. Sixth and last, we discuss the analytical method and conclude with a discussion of the benefits of set theoretical approaches based on the social philosophical approach of associational sociology.

Publication information

Original languageEnglish
Title of host publicationProceedings of the IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014
EditorsManfred Reichert, Stefanie Rinderle-Ma, Georg Grossmann
Place of PublicationLos Alamitos, CA
Publication date2014
ISBN (Print)9781479954704
StatePublished - 2014
EventThe 18th IEEE Enterprise Computing Conference. EDOC 2014 - Ulm, Germany
Duration: 1 Sep 20145 Sep 2014
Conference number: 18


ConferenceThe 18th IEEE Enterprise Computing Conference. EDOC 2014
SeriesInternational Enterprise Distributed Object Computing Conference. Proceedings

ID: 43895740