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A Hybrid Sentiment Analysis Method

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The 10th International Conference on Computer Engineering and Networks (CENet 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1274))

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Abstract

Sentiment analysis has attracted a wide range of attentions in the last few years. Supervised-based and lexicon-based methods are two mainly sentiment analysis categories. Supervised-based approaches could get excellent performance with sufficient tagged samples, while the acquisition of sufficient tagged samples is difficult to implement in some cases. Lexicon-based method can be easily applied to variety domains but excellent quality lexicon is needed, otherwise it will get unsatisfactory performance. In this paper, a hybrid supervised review sentiment analysis method which takes advantage of both of the two categories methods is proposed. In training phrase, lexicon-based method is used to learn confidence parameters which used to determine classifier selection from a small-scale labeled dataset. Then training set which is used to train a Naive Bayes sentiment classifier. Finally, a sentiment analysis framework consist of the lexicon-based sentiment polarity classifier and the learned Naive Bayes classifier is constructed. The optimal hybrid classifier is obtained by obtaining the optimal threshold value. Experiments are conducted on four review datasets.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (61672179, 61370083 and 61402126), the Natural Science Foundation of Heilongjiang Province (F2015030), the Youth Science Foundation of Heilongjiang Province of China (QC2016083) and Heilongjiang Postdoctoral Fund (LBH-Z14071, LBH-Z19015), the China Postdoctoral Science Foundation (2019M651262), and the Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education of China (20YJCZH172) and the Fundamental Research Funds for the Central Universities (3072020CF0605).

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Authors and Affiliations

  1. Harbin Engineering University, Harbin, 150001, China

    Hongyu Han, Yongshi Zhang, Jianpei Zhang, Jing Yang & Yong Wang

Authors
  1. Hongyu Han
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  2. Yongshi Zhang
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  3. Jianpei Zhang
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  4. Jing Yang
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  5. Yong Wang
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Corresponding author

Correspondence to Yongshi Zhang .

Editor information

Editors and Affiliations

  1. Science and Technology, Nanjing University of Information, Nanjing, Jiangsu, China

    Qi Liu

  2. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Xiaodong Liu

  3. School of Electrical Engineering, University of Jinan, Jinan, Shandong, China

    Tao Shen

  4. Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, Beijing, China

    Xuesong Qiu

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Han, H., Zhang, Y., Zhang, J., Yang, J., Wang, Y. (2021). A Hybrid Sentiment Analysis Method. In: Liu, Q., Liu, X., Shen, T., Qiu, X. (eds) The 10th International Conference on Computer Engineering and Networks. CENet 2020. Advances in Intelligent Systems and Computing, vol 1274. Springer, Singapore. https://doi.org/10.1007/978-981-15-8462-6_130

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  • DOI: https://doi.org/10.1007/978-981-15-8462-6_130

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8461-9

  • Online ISBN: 978-981-15-8462-6

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