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.
Original language | English |
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Title of host publication | CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management |
Pages | 2455-2458 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 19 Dec 2012 |
Event | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: 29 Oct 2012 → 2 Nov 2012 |
Conference
Conference | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 |
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Country | United States |
City | Maui, HI |
Period | 29/10/12 → 2/11/12 |
Keywords
- emotion classification
- machine learning
- multi-label classification
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications