Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

Xin Kang; Fuji Ren; Yunong Wu

doi:10.1109/JAS.2017.7510421

Volume 5 Issue 1

Jan. 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(1): 204-216

Xin Kang, Fuji Ren and Yunong Wu, "Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 204-216, Jan. 2018. doi: 10.1109/JAS.2017.7510421

Citation:

Xin Kang, Fuji Ren and Yunong Wu, "Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 204-216, Jan. 2018. doi: 10.1109/JAS.2017.7510421

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Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

doi: 10.1109/JAS.2017.7510421

Faculty of Engineering, Tokushima University, 2-1, Minamijyousanjima-cho, Tokushima 770-8506, Japan

Funds:

the National Natural Science Foundation of China (NSFC) Key Program 61573094

Fundamental Research Funds for the Central Universities N140402001

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Author Bio:
Xin Kang (M'16) received the Ph.D degree from Tokushima University, Tokushima, Japan, in 2013, the M.E. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2009, and the B.E. degree from Northeastern University, Shenyang, China, in 2006. He is currently an Assistant Professor in Tokushima University, Japan. His research interests include statistical machine learning, probabilistic graphical models, neural networks, and text emotion prediction. (e-mail: kang-xin@is.tokushima-u.ac.jp)

Fuji Ren (SM'03) was born in 1959, in China. He received the B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 1982 and 1985, respectively. He received his Ph.D. degree in 1991 from Hokkaido University, Japan. From 1991, he worked at CSK, Japan, where he was a Chief Researcher of NLP. From 1994 to 2000, he was an Associate Professor in the Faculty of Information Sciences, Hiroshima City University. He became a Professor in the Faculty of Engineering of the University of Tokushima, Japan, in 2001. His research interests include natural language processing, artificial intelligence, language understanding and communication, and affective computing. He is a Member of IEICE, CAAI, IEEJ, IPSJ, JSAI, and AAMT. He is a Fellow of the Japan Federation of Engineering Societies. He is the President of the International Advanced Information Institute. (e-mail: ren@is.tokushima-u.ac.jp)

Yunong Wu received the Ph. D degree and Master degree from The University of Tokushima, Tokushima, Japan, in 2014 and 2011, respectively. He is currently an Assistant Professor in Tokushima University, Japan. His research interests include statistical machine learning, affective information computing, neural network and probabilistic graphical model. (e-mail: wu-yunong@tokushima-u.ac.jp)
Corresponding author: Xin Kang, e-mail:kang-xin@is.tokushima-u.ac.jp
Received Date: 2016-05-20
Accepted Date: 2016-09-20

Abstract

Abstract

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.
- Bayesian inference,
- emotion-topic model,
- emotion recognition,
- multi-label classification,
- natural language understanding

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References(33)

References

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Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

doi: 10.1109/JAS.2017.7510421

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