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Enhanced Twofold-LDA Model for Aspect Discovery and Sentiment Classification

Author

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  • Nicola Burns

    (Genesys, Frimley, UK)

  • Yaxin Bi

    (Ulster University, Antrim, UK)

  • Hui Wang

    (Ulster University, Antrim, UK)

  • Terry Anderson

    (Ulster University, Antrim, UK)

Abstract

There is a need to automatically classify information from online reviews. Customers want to know useful information about different aspects of a product or service and also the sentiment expressed towards each aspect. This article proposes an Enhanced Twofold-LDA model (Latent Dirichlet Allocation), in which one LDA is used for aspect assignment and another is used for sentiment classification, aiming to automatically determine aspect and sentiment. The enhanced model incorporates domain knowledge (i.e., seed words) to produce more focused topics and has the ability to handle two aspects in at the sentence level simultaneously. The experiment results show that the Enhanced Twofold-LDA model is able to produce topics more related to aspects in comparison to the state of arts method ASUM (Aspect and Sentiment Unification Model), whereas comparable with ASUM on sentiment classification performance.

Suggested Citation

  • Nicola Burns & Yaxin Bi & Hui Wang & Terry Anderson, 2019. "Enhanced Twofold-LDA Model for Aspect Discovery and Sentiment Classification," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 9(4), pages 1-20, October.
    • Handle: RePEc:igg:jkbo00:v:9:y:2019:i:4:p:1-20
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    Cited by:

    1. Yaxin Bi, 2022. "Sentiment classification in social media data by combining triplet belief functions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 968-991, July.

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