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3WS-ITSC: Three-Way Sampling on Imbalanced Text Data for Sentiment Classification

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Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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Abstract

Sentiment analysis is an important research direction of natural language processing. The data imbalance is a critical issue in text sentiment classification task. That arises the problem of high misclassification cost. This paper proposes a three-way sampling sentiment classification model for imbalanced text data to reduce the misclassification cost. Specifically, the model extracts boundary points through three-way sampling and collaborates with cost-sensitive learning for action on sampled results. Firstly, in order to reduce sampling time, the text data is converted into a one-dimensional vector by bag mapping. Secondly, three-way sampling is used to obtain boundary points that can characterize the majority class. Finally, a sequential three-way sentiment classification algorithm is used to predict sentiment polarity. The experimental results show that the proposed model outperforms state-of-the-art sentiment classification methods in the scenario of extremely imbalanced test data.

This work was supported by the National Natural Science Foundation of China (62006200); The Southwest Petroleum University Postgraduate English Course Construction Project (No. 2020QY04); Central Government Funds of Guiding Local Scientific and Technological Development (No. 2021ZYD0003).

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Notes

  1. 1.

    http://ai.stanford.edu/~amaas/data/sentiment/.

  2. 2.

    https://www.yelp.com/dataset.

  3. 3.

    http://snap.stanford.edu/data/web-Amazon-links.html.

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Author information

Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China

    Yu Fang, Zhao-Chen Li & Fan Min

  2. School of Computer, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor, Malaysia

    Yu Fang

  3. Department of Artificial Intelligence, School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Xin Yang

  4. Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Xin Yang

  5. Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, China

    Fan Min

Authors
  1. Yu Fang
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  2. Zhao-Chen Li
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  3. Xin Yang
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  4. Fan Min
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Corresponding author

Correspondence to Fan Min .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Fang, Y., Li, ZC., Yang, X., Min, F. (2022). 3WS-ITSC: Three-Way Sampling on Imbalanced Text Data for Sentiment Classification. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_30

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

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