Detecting Concept-level Emotion Cause in Microblogging

TL;DR

This paper introduces a Concept-level Emotion Cause Model (CECM) that leverages topic modeling and PageRank to identify emotion causes in microblogging data, outperforming baseline methods.

Contribution

The paper presents a novel concept-level approach using a modified topic model and topical PageRank for emotion cause detection in microblogs, improving accuracy over existing word-level models.

Findings

CECM outperforms baseline methods in detecting emotion causes

The model effectively identifies multiword expressions as causes

Experimental results on Sina Weibo data validate the approach

Abstract

In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event. A modified topic-supervised biterm topic model is utilized in CECM to detect emotion topics' in event-related tweets, and then context-sensitive topical PageRank is utilized to detect meaningful multiword expressions as emotion causes. Experimental results on a dataset from Sina Weibo, one of the largest microblogging websites in China, show CECM can better detect emotion causes than baseline methods.