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Mining Feature-Opinion from Reviews Based on Dependency Parsing

Tieke He

State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, P. R. China

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Rui Hao

State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, P. R. China

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Hang Qi

State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, P. R. China

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Jia Liu

State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, P. R. China

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, and

Qing Wu

School of Economics, Nanjing University, Nanjing, Jiangsu 210023, P. R. China

E-mail Address: wuqing@nju.edu.cn

Corresponding author.

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https://doi.org/10.1142/S0218194016710029Cited by:1 (Source: Crossref)

Abstract

The manual reading of all the product reviews to find a satisfying item is not only labor-intensive, but also tedious for the consumers. In this paper, we propose a feature-opinion mining approach to automatically summarize the reviews, which is based on dependency parsing. Specifically, in our approach we first utilize a regression model to generate sentiment word, including phrase and its sentiment weight, and then we extract the feature based on the dependency relationship between feature word and sentiment word, finally we assign a score to the feature according to the dependency relationship. The experimental results demonstrate that our approach can effectively mine the feature-opinion from reviews.

Keywords:

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