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Visualizing Emotions from Chinese Blogs by Textual Emotion Analysis and Recognition Techniques

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  • Changqin Quan

    (AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, HeFei University of Technology, Tunxi Road No. 193 Hefei, 230009, China)

  • Fuji Ren

    (Faculty of Engineering, University of Tokushima, 2-1 Minamijosanjima, Tokushima 770-8506, Japan)

Abstract

The research on blog emotion analysis and recognition has become increasingly important in recent years. In this study, based on the Chinese blog emotion corpus (Ren-CECps), we analyze and compare blog emotion visualization from different text levels: word, sentence, and paragraph. Then, a blog emotion visualization system is designed for practical applications. Machine learning methods are applied for the implementation of blog emotion recognition at different textual levels. Based on the emotion recognition engine, the blog emotion visualization interface is designed to provide a more intuitive display of emotions in blogs, which can detect emotion for bloggers, and capture emotional change rapidly. In addition, we evaluated the performance of sentence emotion recognition by comparing five classification algorithms under different schemas, which demonstrates the effectiveness of the Complement Naive Bayes model for sentence emotion recognition. The system can recognize multi-label emotions in blogs, which provides a richer and more detailed emotion expression.

Suggested Citation

  • Changqin Quan & Fuji Ren, 2016. "Visualizing Emotions from Chinese Blogs by Textual Emotion Analysis and Recognition Techniques," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 215-234, January.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:01:n:s0219622014500710
    DOI: 10.1142/S0219622014500710
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    References listed on IDEAS

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    1. F. J. Ren, 2005. "Automatic Abstracting Important Sentences," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 141-152.
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