With the growth of social media usage, the study of online communities and groups has become an appealing research domain. In this context, grouping like-minded users is one of the emerging problems. Indeed, it gives a good idea about group formation and evolution, explains various social phenomena and leads to many applications, such as link prediction and product suggestion. In this dissertation, we propose a novel unsupervised method for grouping like-minded users within social networks. Such a method detects groups of users sharing the same interest centers and having similar opinions. In fact, the proposed method is based on extracting the interest centers and retrieving the polarities from the user’s textual posts.
CITATION STYLE
Jaffali, S., Jamoussi, S., & Hamadou, A. B. (2014). Grouping like-minded users based on text and sentiment analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8733, 83–93. https://doi.org/10.1007/978-3-319-11289-3_9
Mendeley helps you to discover research relevant for your work.