Sentiment-driven Community Profiling and Detection on Social Media

TL;DR

This paper introduces a sentiment-driven framework for community detection on social media that profiles communities based on collective positive and negative opinions, considering user attitudes and social interactions.

Contribution

It presents a novel approach that incorporates sentiment analysis into community profiling, addressing the limitations of keyword-based methods by capturing user attitudes and social dynamics.

Findings

Effective community profiling based on sentiment analysis.

Improved clustering of users by opinions and interactions.

Validated through quantitative and qualitative evaluations.

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.