Finding Emotional Focus for Emotion Recognition at Sentence Level

QUAN Changqin; REN Fuji

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QUAN Changqin and REN Fuji, “Finding Emotional Focus for Emotion Recognition at Sentence Level,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 99-103, 2013,

Citation:

QUAN Changqin and REN Fuji, “Finding Emotional Focus for Emotion Recognition at Sentence Level,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 99-103, 2013,

QUAN Changqin and REN Fuji, “Finding Emotional Focus for Emotion Recognition at Sentence Level,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 99-103, 2013,

Citation:

QUAN Changqin and REN Fuji, “Finding Emotional Focus for Emotion Recognition at Sentence Level,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 99-103, 2013,

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Finding Emotional Focus for Emotion Recognition at Sentence Level

1.
Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230009, China;
2.
Faculty of Engineering, The University of Tokushima, Tokushima, Japan

Funds: This work is partially supported by the National High Technoligy Research and Development Program of Chna (863 Program) (No.2012AA011103).

Received Date: 2011-12-01
Rev Recd Date: 2012-02-01
Publish Date: 2013-01-05

Abstract

Abstract

Emotion recognition at sentence level is one of the fundamental problems of textual emotion understanding. Based on the observation that sentence emotional focus can be expressed by some clauses in this sentence, this paper proposes to find the emotional focus for sentence emotion recognition. For the sake of breaking through the problems brought about by depending on emotion lexicons, we first recognize word emotions in a sentence based on Maximum entropy model. And then homogeneous Markov model is built for clause emotion recognition; After that, a strategy based on emotion selection is proposed for a sentence with multiple clauses, and genetic algorithm is used for clause selection by textual feature weighting. The experimental results show that, comparing with the baseline, there are 9.1% and 3.6% improvement respectively for two different evaluations. It is demonstrated that finding emotional focus by clause selection is able to improve the performance of sentence emotion recognition significantly.
- Sentence emotion recognition,
- Feature extraction,
- Maximum entropy,
- Homogeneous Markov model,
- Genetic algorithm

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References(20)

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