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Artificial Intelligence Review

Erschienen in:

03.01.2020

An implicit opinion analysis model based on feature-based implicit opinion patterns

verfasst von: Zhao Fang, Qiang Zhang, Xiaoan Tang, Anning Wang, Claude Baron

Erschienen in: Artificial Intelligence Review | Ausgabe 6/2020

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Abstract

With the rapid growth of social networks, mining customer opinions based on online reviews is crucial to understand consumer needs. Due to the richness of language expressions, customer opinions are often expressed implicitly. However, previous studies usually focus on mining explicit opinions to understand consumer needs. In this paper, we propose a novel implicit opinion analysis model to perform implicit opinion analysis of Chinese customer reviews at both the feature and review levels. First, we extract an implicit-opinionated review/clause dataset from raw review dataset and introduce the concept of the feature-based implicit opinion pattern (FBIOP). Secondly, we develop a clustering algorithm to construct product feature categories. Based on the constructed feature categories, FBIOPs can be mined from the extracted implicit-opinionated clause dataset. Thirdly, the sentiment intensity and polarity of each FBIOP are calculated by using the Chi squared test and pointwise mutual information. Fourthly, according to the resulting FBIOP polarities, the polarities of implicit opinions can be determined at both the feature and review levels. Car forum reviews written in Chinese are collected and labeled as the experimental dataset. The results show that the proposed model outperforms the traditional support vector machine model and the cutting-edge convolutional neural network model.

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Titel: An implicit opinion analysis model based on feature-based implicit opinion patterns
verfasst von: Zhao Fang
Qiang Zhang
Xiaoan Tang
Anning Wang
Claude Baron
Publikationsdatum: 03.01.2020
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 6/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-019-09801-9

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