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arXiv · 1504.08050

Detecting Concept-level Emotion Cause in Microblogging

Shuangyong Song·Yao Meng
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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.

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Shuangyong Song, Yao Meng. 2015-04-30. Detecting Concept-level Emotion Cause in Microblogging. https://arxiv.org/abs/1504.08050

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