  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.
