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Dynamic Forest Model for Sentiment Classification

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Book cover Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Sentiment classification is a useful approach to analyse the emotional polarity of user reviews, and method based on machine learning has achieved a great success. In the era of Web2.0, the emotional intensity of terms will change with time and events, while a large number of Out-Of-Vocabulary (OOV) terms are appearing. But the method of machine learning pays little attention to them because they focus to reduce the computational complexity. To address the problem, we proposed a dynamic forest model, which can describe the emotional intensity of the term in character granularity, and can append OOV dynamically and adjust their emotional intensity value. Experiments show that in the Chinese environment, our model greatly boosts the performance compared with the method based machine learning, while the time is saved by halves.

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Notes

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    http://www.datatang.com/data/11837.

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    http://www.keenage.com.

  3. 3.

    http://yynl.jsnu.edu.cn/_t307/0c/b4/c541a3252/page.htm.

  4. 4.

    http://www.datatang.com.

  5. 5.

    http://www.sogou.com/labs/resource/cs.php.

  6. 6.

    http://licstar.net/archives/262.

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Acknowledgments

We would like to thank Sougou for its news data.

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Mingming Li, Jiao Dai, Wei Liu & Jizhong Han

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Mingming Li

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  1. Mingming Li
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  2. Jiao Dai
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  3. Wei Liu
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  4. Jizhong Han
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Corresponding author

Correspondence to Jiao Dai .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Li, M., Dai, J., Liu, W., Han, J. (2017). Dynamic Forest Model for Sentiment Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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