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Research on feature-based opinion mining using topic maps

Lixin Xia (School of Information Management, Central China Normal University, Wuhan City, People’s Republic of China)
Zhongyi Wang (School of Information Management, Central China Normal University, Wuhan City, People’s Republic of China)
Chen Chen (School of Information Management, Central China Normal University, Wuhan City, People’s Republic of China)
Shanshan Zhai (School of Information Management, Central China Normal University, Wuhan City, People’s Republic of China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 6 June 2016

609

Abstract

Purpose

Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems.

Design/methodology/approach

Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words.

Findings

In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions.

Research limitations/implications

To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines.

Originality/value

This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

Keywords

Acknowledgements

This study is supported by National Social Science Foundation of China: “Research on Multi-granularity Integration Knowledge Services of Digital Library Based on Linked Data” (14CTQ003) and is the major project of National Social Science Foundation of China: “Research on Knowledge Discovery of Internet Resource base on Multi-dimensional Aggregation” (No. 13&ZD183).

Citation

Xia, L., Wang, Z., Chen, C. and Zhai, S. (2016), "Research on feature-based opinion mining using topic maps", The Electronic Library, Vol. 34 No. 3, pp. 435-456. https://doi.org/10.1108/EL-11-2014-0197

Publisher

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Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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