Emotion Classification by Incremental Association Language Features
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.
[1] A. Khoo, Y. Marom and D. Albrecht, "Experiments with Sentence
Classification," In Proc. of Australasian Language Technology
Workshop, 2006, pp. 18-25.
[2] American Psychiatric Association, Diagnostic and statistical manual of
mental disorders (4th ed.). Washington, DC: Author, 1994.
[3] 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.
[4] F. Sebastiani, "Machine Learning in Automated Text Categorization,"
ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
[5] 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.
[6] 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.
[7] I. H. Witten and E Frank, Data Mining: Practical Machine Learning
Tools and Techniques (2nd ed.), Elsevier, San Francisco, 2005
[8] J. Han and M. Kamber, Data Mining: Concepts and Techniques (2nd ed.).
Morgan Kaufmann,San Francisco, 2007.
[9] 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.
[10] 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.
[11] 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.
[12] M. Naughton, N. Stokes, and J. Carthy, "Investigating Statistical
Techniques for Sentence-Level Event Classification," In Proc. of
COLING-08, 2008, pp. 617-624.
[13] 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.
[14] 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.
[15] S. Li and C. Zong, "Multi-domain Sentiment Classification, " In Proc. of
ACL-08, 2008, pp. 257-260.
[16] 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.
[1] A. Khoo, Y. Marom and D. Albrecht, "Experiments with Sentence
Classification," In Proc. of Australasian Language Technology
Workshop, 2006, pp. 18-25.
[2] American Psychiatric Association, Diagnostic and statistical manual of
mental disorders (4th ed.). Washington, DC: Author, 1994.
[3] 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.
[4] F. Sebastiani, "Machine Learning in Automated Text Categorization,"
ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
[5] 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.
[6] 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.
[7] I. H. Witten and E Frank, Data Mining: Practical Machine Learning
Tools and Techniques (2nd ed.), Elsevier, San Francisco, 2005
[8] J. Han and M. Kamber, Data Mining: Concepts and Techniques (2nd ed.).
Morgan Kaufmann,San Francisco, 2007.
[9] 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.
[10] 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.
[11] 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.
[12] M. Naughton, N. Stokes, and J. Carthy, "Investigating Statistical
Techniques for Sentence-Level Event Classification," In Proc. of
COLING-08, 2008, pp. 617-624.
[13] 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.
[14] 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.
[15] S. Li and C. Zong, "Multi-domain Sentiment Classification, " In Proc. of
ACL-08, 2008, pp. 257-260.
[16] 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.
@article{"International Journal of Information, Control and Computer Sciences:50025", author = "Jheng-Long Wu and Pei-Chann Chang and Shih-Ling Chang and Liang-Chih Yu and Jui-Feng Yeh and Chin-Sheng Yang", title = "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.", keywords = "Association language features, Emotion Classification,Overlap-Category Feature, Nature Language Processing.", volume = "4", number = "5", pages = "877-5", }