A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
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Article Contents
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

Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

doi: 10.1109/JAS.2017.7510421
Funds:

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

Fundamental Research Funds for the Central Universities N140402001

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  • 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.

     

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