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A two-stage unsupervised sentiment analysis method

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Abstract

In this paper, the SASC (Sentiment Analysis based on Sentiment Clustering) method is proposed to solve the problems of low accuracy and poor stability in the review sentiment clustering methods. Through two-stage sentiment clustering, the hidden sentiment information among the review texts is obtained to improve the accuracy and stability of the results. Specifically, in the first stage, the review representation vector construction method is put forward through the topic model LDA. Then the second stage uses K-means algorithm to achieve further optimization of the sentiment clustering results. In the experiment part, the evaluation methods of sentiment clustering are firstly introduced, and then a series of experiments are carried out on two widely used datasets Large Movie Review Dataset v1.0 and Multi-Domain Sentiment Dataset. Experiment results indicate that compared with other methods, the SASC method proposed in this paper has better clustering accuracy and stability.

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Data availability

The datasets analysed during the current study are available in the Multi-Domain Sentiment Dataset (version 2.0) [https://www.cs.jhu.edu/~mdredze/datasets/sentiment/] and the Large Movie Review Dataset v1.0 [http://ai.stanford.edu/~amaas/data/sentiment/].

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Funding

The research leading to these results received funding from the Key Research and Promotion Projects of Henan Province under Grant Agreement No (222102210034, 222102210178, and 212102210099), and the Key Research Projects of Henan Higher Education Institutions under Grant Agreement No 22A520020.

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Authors and Affiliations

  1. College of Software, Henan University, Kaifeng, 475004, China

    Yingqi Wang, Xin He & Rui Zhai

  2. Henan Provincial Engineering Research Center of Intelligent Data Processing, Henan University, Kaifeng, China

    Yingqi Wang & Rui Zhai

  3. College of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Hongyu Han

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  1. Yingqi Wang
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Correspondence to Xin He.

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Wang, Y., Han, H., He, X. et al. A two-stage unsupervised sentiment analysis method. Multimed Tools Appl 82, 26527–26544 (2023). https://doi.org/10.1007/s11042-023-14864-6

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  • DOI: https://doi.org/10.1007/s11042-023-14864-6

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