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Multi-granular document-level sentiment topic analysis for online reviews

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Abstract

It is key to identify both sentiment and topic for well understanding and managing social media data such as online reviews and microblogs. This paper studies a robust and reliable solution for synchronous analysis of sentiment and topic in online reviews. Specifically, a probabilistic model is proposed for joint sentiment topic detection with multi-granular computation, named MgJST (multi-granular joint sentiment topic). The MgJST model introduces sentence level structural knowledge to detect sentiment and topic simultaneously from reviews based on latent Dirichlet allocation (LDA). The sets of experiments are conducted on seven sentiment analysis datasets. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art unsupervised approaches WSTM and STSM in terms of sentiment detection quality, and has powerful ability to extract topics from reviews.

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Notes

  1. https://www.kaggle.com/iarunava/imdb-movie-reviews-dataset

  2. http://www.nltk.org/

  3. http://www.tripadvisor.cn

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Acknowledgements

This work was supported by the Natural Science Foundation of China under Grant 61962038, Grant 61962006, and by “BAGUI Scholar” Program of Guangxi Zhuang Autonomous Region of China under Grant 201979, and by the Foreign Cooperation Project of Fujian Provincial Department of Science and Technology under Grant 2020I0014, and by the Startup Project of Doctoral Research of Fujian Normal University.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Nanning Normal University, Nanning, 530001, China

    Faliang Huang, Yingzhou Bi & Jianbo Lu

  2. Guangxi Academy of Sciences, Nanning, 530003, China

    Changan Yuan

  3. School of Information Engineering, Lingnan Normal University, Zhanjiang, 524048, China

    Liqiong Lu

  4. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, China

    Xing Wang

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  1. Faliang Huang
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  2. Changan Yuan
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  3. Yingzhou Bi
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  4. Jianbo Lu
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  5. Liqiong Lu
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  6. Xing Wang
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Correspondence to Xing Wang.

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Huang, F., Yuan, C., Bi, Y. et al. Multi-granular document-level sentiment topic analysis for online reviews. Appl Intell 52, 7723–7733 (2022). https://doi.org/10.1007/s10489-021-02817-1

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