Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral

Online reviews contain a wealth of information about customers' concerns and sentiments. Sentiment analysis can mine consumer preferences and satisfaction over products/services. Most existing studies on sentiment analysis only considered how to extract attribute types or attribute values of products/services from textual reviews, but ignored the role of attribute-level ratings in reflecting consumer preferences and satisfaction. Based on sentiment analysis and preference disaggregation, this paper unifies the quantitative and qualitative information extracted from attribute-level ratings and textual reviews, respectively, to obtain attribute types and attribute values of products/services. To acquire individual consumer preferences concerning product/service attributes, this paper proposes a method within an aggregation-disaggregation paradigm based on the attitudinal Choquet integral to transform overall online ratings into the form of pairwise comparisons. Compared with the additive value function used in most studies, more consumer preferences in terms of the importance of attributes, the interactions between pairwise attributes, and the tolerance of consumers to make compensation between attribute values in the aggregation process can be deduced by our proposed method. Several real cases on TripAdvisor.com are given to show the applicability of the proposed method.