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Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach
- Source :
- Cluster Computing. 22:12619-12632
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
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.
- Subjects :
- Cognitive evaluation theory
Computer Networks and Communications
Latent semantic analysis
business.industry
Computer science
media_common.quotation_subject
Sentiment analysis
020206 networking & telecommunications
02 engineering and technology
computer.software_genre
Disgust
Support vector machine
Helpfulness
0202 electrical engineering, electronic engineering, information engineering
Happiness
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Natural language processing
media_common
Subjects
Details
- ISSN :
- 15737543 and 13867857
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Cluster Computing
- Accession number :
- edsair.doi...........aacddea9ad7e939f1ade9d2ae2889f4e