Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts

Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a...

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Bibliographic Details
Published in:Electronic library 2020-03, Vol.38 (1), p.155-169
Main Authors: Hung, Chihli, Cao, You-Xin
Format: Article
Language:English
Subjects:
Academic Achievement
Activity Units
Benchmarks
Chinese
Classification
Collocation methods
Consumer satisfaction
Cosmetics
Data mining
Decision making
Decision trees
Documents
Domains
Evaluation
Internet
Modelling
Multilayer perceptrons
Negative Attitudes
Opinions
Preferences
Research Methodology
Researchers
Romanization
Semantics
Semiotics
Sentiment analysis
Support vector machines
Teaching Methods
User generated content
Word of mouth advertising
Word Processing
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Summary:Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.
ISSN:0264-0473
1758-616X
1758-616X
DOI:10.1108/EL-04-2019-0093