Coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context

Authors:
Jun Xu;Ruifeng Xu;Qin Lu;Xiaolong Wang
Affiliations:
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

This paper proposes a novel approach using a coarse-to-fine analysis strategy for sentence-level emotion classification which takes into consideration of similarities to sentences in training set as well as adjacent sentences in the context. First, we use intra-sentence based features to determine the emotion label set of a target sentence coarsely through the statistical information gained from the label sets of the k most similar sentences in the training data. Then, we use the emotion transfer probabilities between neighboring sentences to refine the emotion labels of the target sentences. Such iterative refinements terminate when the emotion classification converges. The proposed algorithm is evaluated on Ren-CECps, a Chinese blog emotion corpus. Experimental results show that the coarse-to-fine emotion classification algorithm improves the sentence-level emotion classification by 19.11% on the average precision metric, which outperforms the baseline methods.