Abstract

Web 2.0 helps to expand the range and depth of conversation on many issues and facilitates the formation of online communities. Online communities draw various individuals together based on their common opinions on a core set of issues. Most existing community detection methods merely focus on discovering communities without providing any insight regarding the collective opinions of community members and the motives behind the formation of communities. Several efforts have been made to tackle this problem by presenting a set of keywords as a community profile. However, they neglect the positions of community members towards keywords, which play an important role for understanding communities in the highly polarized atmosphere of social media. To this end, we present a sentiment-driven community profiling and detection framework which aims to provide community profiles presenting positive and negative collective opinions of community members separately. With this regard, our framework initially extracts key expressions in users' messages as representative of issues and then identifies users' positive/negative attitudes towards these key expressions. Next, it uncovers a low-dimensional latent space in order to cluster users according to their opinions and social interactions (i.e., retweets). We demonstrate the effectiveness of our framework through quantitative and qualitative evaluations.

Original languageEnglish (US)
Title of host publicationHT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages229-237
Number of pages9
ISBN (Electronic)9781450354271
DOIs
StatePublished - Jul 3 2018
Event29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States
Duration: Jul 9 2018Jul 12 2018

Publication series

NameHT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media

Conference

Conference29th ACM International Conference on Hypertext and Social Media, HT 2018
CountryUnited States
CityBaltimore
Period7/9/187/12/18

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Salehi, A., Ozer, M., & Davulcu, H. (2018). Sentiment-driven community profiling and detection on social media. In HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media (pp. 229-237). (HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209542.3209565