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A Discriminative Approach to Sentiment Classification

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Abstract

Due to the explosive growth of user-generated contents, understanding opinions (such as reviews on products) generated by Internet users is important for optimizing business decision. To achieve such understanding, this paper investigates a discriminative approach to classifying opinions according to sentiments. The discriminative approach builds a model with the prior knowledge of the categorization information in order to extract meaningful features from the unstructured texts. The prior knowledge includes ratio factors to reinforce terms’ sentiment polarity by using TF-IDF, short for term frequency-inverse document frequency. Experimental results with four datasets show the proposed approach is very competitive, compared with some of the previous works.

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Acknowledgements

This research was supported by Research Foundation of Education Bureau of Hubei Province with Grant No. D20172502. We thank the peer reviewers for great comments.

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Authors and Affiliations

  1. College of Computer Science Technology, Hubei Normal University, Huangshi, China

    Guangmin Li

  2. School of Computing, Ulster University, Belfast, BT37 0QB, UK

    Zhiwei Lin, Hui Wang & Xin Wei

Authors
  1. Guangmin Li
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  2. Zhiwei Lin
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  3. Hui Wang
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  4. Xin Wei
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Correspondence to Guangmin Li.

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Li, G., Lin, Z., Wang, H. et al. A Discriminative Approach to Sentiment Classification. Neural Process Lett 51, 749–758 (2020). https://doi.org/10.1007/s11063-019-10108-7

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