Augmenting feature model through customer preference mining by hybrid sentiment analysis
作者：
Highlights：
• We use sentiment analysis of online product reviewers to extract customer preference information.
• The proposed sentiment analysis method is a hybrid combination of various affective lexicons.
• We adopt the commented features from product users to enhance the basic feature.
• We incorporate the customer preference information as attribute into the model.
• We demonstrate the feasibility and potential of the proposed method via an application case.
摘要
•We use sentiment analysis of online product reviewers to extract customer preference information.•The proposed sentiment analysis method is a hybrid combination of various affective lexicons.•We adopt the commented features from product users to enhance the basic feature.•We incorporate the customer preference information as attribute into the model.•We demonstrate the feasibility and potential of the proposed method via an application case.
论文关键词：Feature model,Customer preference mining,Sentiment analysis,Product line planning
论文评审过程：Received 25 August 2016, Revised 26 June 2017, Accepted 14 July 2017, Available online 19 July 2017, Version of Record 24 August 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.07.021
