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

Author:

Sanglerdsinlapachai Nuttapong1,Plangprasopchok Anon2,Ho Tu Bao3,Nantajeewarawat Ekawit4

Affiliation:

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

2. National Electronics and Computer Technology Center, Pathumthani, Thailand

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

4. 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.

Publisher

IGI Global

Subject

Artificial Intelligence,Management of Technology and Innovation,Information Systems and Management,Organizational Behavior and Human Resource Management,Strategy and Management,Information Systems

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3. Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types;Artificial Intelligence in Medicine;2021-03

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