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Cluster Computing
Published in: Cluster Computing 5/2019

16-01-2018

Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach

Authors: Wei Zhang, Sui-xi Kong, Yan-chun Zhu, Xiao-le Wang

Published in: Cluster Computing | Special Issue 5/2019

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Abstract

For the current online reviews sentiment classification method, there are some problems such as serious text sparseness and coarse granularity of sentiment calculation. In this paper, the emotion in online reviews is divided into four categories: happiness, hope, disgust, and anxiety. Based on the combination of cognitive evaluation theory and sentiment analysis, a novel approach that combines a well-known techniques to sentiment classification, ie, support vector machine and the latent semantic analysis, was proposed. Based on the approach, this paper explored the influence of these four kinds of emotions on the helpfulness of online reviews, examined the moderating effects of emotion on the helpfulness of online reviews under the two types of products. The experimental results showed that this model could effectively conduct multi-emotion fine-grained computing for online reviews, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that happiness and disgust emotion had significant positive impact on the helpfulness of online reviews, while on the other hand anxiety emotion had significant negative influence. The algorithm and its empirical conclusions provide useful theoretical basis and reference for the company to optimize marketing strategy and improve customer relationship under web 2.0.
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Metadata
Title
Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach
Authors
Wei Zhang
Sui-xi Kong
Yan-chun Zhu
Xiao-le Wang
Publication date
16-01-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1693-7

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