Commenced in January 2007
Paper Count: 30184
Emotion Classification by Incremental Association Language Features
Abstract:The Major Depressive Disorder has been a burden of medical expense in Taiwan as well as the situation around the world. Major Depressive Disorder can be defined into different categories by previous human activities. According to machine learning, we can classify emotion in correct textual language in advance. It can help medical diagnosis to recognize the variance in Major Depressive Disorder automatically. Association language incremental is the characteristic and relationship that can discovery words in sentence. There is an overlapping-category problem for classification. In this paper, we would like to improve the performance in classification in principle of no overlapping-category problems. We present an approach that to discovery words in sentence and it can find in high frequency in the same time and can-t overlap in each category, called Association Language Features by its Category (ALFC). Experimental results show that ALFC distinguish well in Major Depressive Disorder and have better performance. We also compare the approach with baseline and mutual information that use single words alone or correlation measure.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329238Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1477
 A. Khoo, Y. Marom and D. Albrecht, "Experiments with Sentence Classification," In Proc. of Australasian Language Technology Workshop, 2006, pp. 18-25.
 American Psychiatric Association, Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author, 1994.
 C. H. Wu, Z. J. Chuang, and Y. C. Lin, "Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models," ACM Trans. Asian Language Information Processing, vol. 5, no. 2, pp. 165-182, 2006.
 F. Sebastiani, "Machine Learning in Automated Text Categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
 G. Mishne, "Experiments with Mood Classification in Blog Posts" In proc. 1st Workshop on Stylistic Analysis of Text for Information Access, at SIGIR 2005, pp. 53-60.
 H. He, J., Y. Xiong, B. Chen, W. Sun and L. Zhao, "Language Feature Mining for Music Emotion Classification via Supervised Learning from Lyrics," ISICA 2008, 2008, pp. 426-435.
 I. H. Witten and E Frank, Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.), Elsevier, San Francisco, 2005
 J. Han and M. Kamber, Data Mining: Concepts and Techniques (2nd ed.). Morgan Kaufmann,San Francisco, 2007.
 L. C. Yu, C. H. Wu, and F. L. Jang, "HAL-based Cascaded Model for Variable-Length Semantic Pattern Induction from Psychiatry Web Resources," in Proc. of the COLING/ACL 2006, Sydney, Australia, 2006, pp. 945-952.
 L. C. Yu, C. H. Wu, J. F. Yeh and Fong-Lin Jang, "HAL-based Evolutionary Inference for Pattern Induction from Psychiatry Web Resources," IEEE Trans. Evolutionary Computation, vol. 12, no. 2, 2008, pp. 160-170.
 L.-C. Yu, C.-L. Chan, C.-H. Wu and C.-C. Lin, "Mining Association Language Patterns for Negative Life Event Classification," In Proc. ACL-IJCNLP 2009 Conference Short Papers, Singapore, 2009, pp. 201-204.
 M. Naughton, N. Stokes, and J. Carthy, "Investigating Statistical Techniques for Sentence-Level Event Classification," In Proc. of COLING-08, 2008, pp. 617-624.
 R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," In Proc. Int-l Conf. Very Large Data Bases (VLDB), 1994. pp. 487-499.
 R. Chitturi and J. H. L. Hansen, "Dialect Classification for online podcasts fusing Acoustic and Language based Structural and Semantic Information," In Proc. of ACL-08, 2008, pp. 21-24.
 S. Li and C. Zong, "Multi-domain Sentiment Classification, " In Proc. of ACL-08, 2008, pp. 257-260.
 Y. M. Bai, C. C. Lin, J. Y. Chen, and W. C. Liu, "Virtual Psychiatric Clinics," American Journal of Psychiatry, vol. 158, no. 7, pp. 1160-1161, 2001.