Finding Emotional Focus for Emotion Recognition at Sentence Level

QUAN Changqin; REN Fuji

Article Contents

Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 99-103

QUAN Changqin, REN Fuji. Finding Emotional Focus for Emotion Recognition at Sentence Level[J]. Chinese Journal of Electronics, 2013, 22(1): 99-103.

Citation:

QUAN Changqin, REN Fuji. Finding Emotional Focus for Emotion Recognition at Sentence Level[J]. Chinese Journal of Electronics, 2013, 22(1): 99-103.

QUAN Changqin, REN Fuji. Finding Emotional Focus for Emotion Recognition at Sentence Level[J]. Chinese Journal of Electronics, 2013, 22(1): 99-103.

Citation:

QUAN Changqin, REN Fuji. Finding Emotional Focus for Emotion Recognition at Sentence Level[J]. Chinese Journal of Electronics, 2013, 22(1): 99-103.

PDF( 377 KB)

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

FullText(HTML)

References(20)

References

F. Ren, “Affective information processing and recognizing humanemotion”, Electronic Notes in Theoretical Computer Science,Vol.225, No.2009, pp.39-50, 2009.

J. Ma, M. Suzuki and F. Ren, “Spokesperson detection methodfor autonomous robot in complex communication environmentbased on image processing”, International Journal of InnovativeComputing, Information and Contraol, Vol.6, No.3(B),pp.1515-1524, 2010.

Y. Zhao, L. Zhao, C. Zou and Y. Yu, “Speech emotion recognitionusing modified quadratic discrimination function”, Journalof Electronics (CHINA), Vol.25, No.6, pp.840-844, 2008.

Y. Xue, X. Mao, C. Caleanu, S. Lv, “Layered fuzzy facial expressiongeneration of virtual agent”, Chinese Journal of Electronics,Vol.19, No.1, pp.69-74, 2010.

C. Strapparava, R. Mihalcea, “Learning to identify emotions intext”, Proc. of the 2008 ACM Symposium on Applied Computing,Fortaleza, Cear, Brazil, pp.1556-1560, 2008.

P.K. Bhowmick, A. Mukherjee, A. Banik, P. Mitra, A. Basu,“A comparative study of the properties of emotional and nonemotionalwords in the Wordnet: A complex network approach”,Proc. of International Conference on Natural LanguageProcessing (ICON 2008), CDAC Pune, India, 2008.

C. Strapparava, A. Valitutti, O. Stock, “Dances with words”,Proc. of the Twentieth International Joint Conference on ArtificialIntelligence (IJCAI 2007), pp.1719-1724, 2007.

C. Quan, F. Ren, “Automatic annotation of word emotion insentences based on Ren-CECps”, Proc. of the Ninth InternationalConference on Language Resources and Evaluation(LREC 2010), Valletta, Malta, 2010.

C. Quan, F. Ren, “An exploration of features for recognizingword emotion”, Proc. of the 23rd International Conferenceon Computational Linguistics (COLING2010), Beijing, pp.922-930, 2010.

C. Quan, F. Ren, “Sentence emotion analysis and recognitionbased on emotion words using Ren-CECps”, International Journalof Advanced Intelligence, Vol.2, No.1, pp.107-119, 2010.

C. Quan, F. Ren and T. He, “Sentimental Classification basedon kernel methods”, International Journal of Innovative Computing,Information and Control, Vol.6, No.6, pp.2681-2690,2010.

H. Liu, H. Lieberman, T. Selker, “A model of textual affectsensing using real-world knowledge”, Proc. of the 2003 InternationalConference on Intelligent User Interfaces, pp.125-132,2003.

C.Wu, Z. Chuang, Y. Lin, “Emotion recognition from text usingsemantic labels and separable mixture models” ACM Transactionson Asian Language Information Processing, Vol.5, No.2,pp.165-183, 2006.

C.Y. Lu, S.H. Lin, J.C. Liu, S. Cruz-Lara, J.S. Hong, “Automaticevent-level textual emotion sensing using mutual actionhistogram between entities”, Expert Systems with Applications,Vol.37, No.2, pp.1643-1653, 2010.

G. Mishne, “Experiments with mood classification in blogposts”, Proc. of the Style2005: The 1st Workshop on StylisticAnalysis of Text for Information Access, SIGIR 2005, Salvador,Brazil, Aug. 2005.

K. Lin, Y.H., C. Yang, H.H. Chen, “What emotions do news articlestrigger in their readers?”, Proc. of the 30th Annual InternationalACM SIGIR Conference, Poster, Amsterdam, Netherland,pp.733-734, 2007.

R. Mihalcea, H. Liu, “A corpus-based approach to finding happiness”,Proc. of AAAI Spring Symposium on ComputationalApproaches to Analyzing Weblogs, Menlo Park, Calif, pp.139-144, 2006.

Y. Jung, H. Park, S.H. Myaeng, “A hybrid mood classificationapproach for blog text”, Lecture Notes in Computer Science,Vol.4099, Berlin: Springer, pp.1099-1103, 2006.

C. Quan, F. Ren, “Emotion analysis in blogs at sentence levelusing a Chinese emotion corpus”, Proc. of IEEE NLP-KE2010,Beijing, pp.427-434, 2010.

C. Quan, F. Ren, “A blog emotion corpus for emotional expressionanalysis in Chinese”, Computer Speech and Language,Vol.24, No.4, pp.726-749, 2010.

Relative Articles

Supplements(0)

Cited By

Proportional views