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Rule-Based Polarity Aggregation Using Rhetorical Structures for Aspect-Based Sentiment Analysis

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  • Nuttapong Sanglerdsinlapachai

    (Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand & Japan Advanced Institute of Science and Technology, Ishikawa, Japan)

  • Anon Plangprasopchok

    (National Electronics and Computer Technology Center, Pathumthani, Thailand)

  • Tu Bao Ho

    (John von Neumann Institute, Vietnam National University, Ho Chi Minh City, Vietnam & Japan Advanced Institute of Science and Technology, Ishikawa, Japan)

  • Ekawit Nantajeewarawat

    (Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand)

Abstract

The segments of a document that are relevant to a given aspect can be identified by using discourse relations of the rhetorical structure theory (RST). Different segments may contribute to the overall sentiment differently, and the sentiment of one segment may affect the contribution of another segment. This work exploits the RST structures of relevant segments to infer the sentiment of a given aspect. An input document is first parsed into an RST tree. For each aspect, relevant segments with their relations in the resulting tree are localized and transformed into a set of features. A set of classification rules is subsequently induced and evaluated on data. The proposed framework performs well in several experimental settings, with the accuracy values ranging from 74.0% to 77.1% being achieved. With proper strategies for removing conflicting rules and tuning the confidence threshold, f-measure values for the negative polarity class can be improved.

Suggested Citation

  • Nuttapong Sanglerdsinlapachai & Anon Plangprasopchok & Tu Bao Ho & Ekawit Nantajeewarawat, 2019. "Rule-Based Polarity Aggregation Using Rhetorical Structures for Aspect-Based Sentiment Analysis," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 10(3), pages 44-60, July.
    • Handle: RePEc:igg:jkss00:v:10:y:2019:i:3:p:44-60
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