Review Helpfulness Prediction Research Based on Review Sentiment Feature Sets
Nie Hui1, Rong Zhe2
1 School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China;
2 Business School, Sun Yat-Sen University, Guangzhou 510275, China
[Objective] Use review sentiment feature sets extracted by dictionary matching method and machine learning method to predict review's helpfulness. [Methods] This paper adopts sentiment dictionary matching method and machine learning classification method to extract review sentiment feature sets, including building sentiment dictionary, designing appropriate matching algorithm and choosing the best sentiment classifier. Random forest algorithm is applied to predict review's helpfulness with different sentiment feature sets. [Results] The combination of two sentiment analysis methods performs best in predicting review helpfulness. Review's average sentiment score and deviation score derived from sentiment dictionary method have better prediction performance to review helpfulness. [Limitations] Only focused on reviews of search product but neglected the reviews of experience product. The research dataset is limited. [Conclusions] The combination of sentiment dictionary matching method and machine learning method can predict review helpfulness effectively.
聂卉, 容哲. 面向评论效用评估的文本情感特征提取[J]. 现代图书情报技术, 2015, 31(7-8): 113-121.
Nie Hui, Rong Zhe. Review Helpfulness Prediction Research Based on Review Sentiment Feature Sets. New Technology of Library and Information Service, 2015, 31(7-8): 113-121.
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