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.
@article{arxiv.1504.08050,
title = {Detecting Concept-level Emotion Cause in Microblogging},
author = {Shuangyong Song and Yao Meng},
journal= {arXiv preprint arXiv:1504.08050},
year = {2015}
}