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With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer's perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a methodology for conducting importance-performance analysis (IPA) through online reviews. The methodology is composed of three stages: (1) mining useful information from online reviews, (2) estimating each attribute's performance and importance, and (3) constructing IPA plot, where the latent dirichlet allocation (LDA), the improved one-vs-one strategy based support vector machine (IOVO-SVM) and the ensemble neural network based model (ENNM) are respectively used. A case study on two five-star hotels is given, and the results obtained by the proposed methodology through online reviews are compared with those obtained by the existing methods through questionnaires (or online ratings). The results indicate that the proposed methodology can obtain effective analysis results with lower cost and shorter time since online reviews are publicly available and easily collected. The proposed methodology can give managers or market analysts one more choice for conducting IPA or serve as a preparing process of large-scale survey. Highlights • A novel methodology for conducting IPA through online reviews is proposed. • The performance of each attribute is estimated according to the sentiment strengths of online reviews. • The importance of each attribute is estimated using ENNM. • Four types of IPA can be easily conducted through online reviews. • A case study of IPA for two five-star hotels is given. [ABSTRACT FROM AUTHOR]

Studies have shown that online product reviews significantly affect consumer purchase decisions. However, it is difficult for the consumer to read online product reviews one by one because the number of online reviews is very large. Thus, to facilitate consumer purchase decisions, how to rank products through online reviews is a valuable research topic. This paper proposes a method for ranking products through online reviews based on sentiment classification and the interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The method consists of two parts: (1) identifying sentiment orientations of the online reviews based on sentiment classification and (2) ranking alternative products based on interval-valued intuitionistic fuzzy TOPSIS. In the first part, the online reviews of the alternative products concerning multiple attributes are preprocessed, and an algorithm based on support vector machine and one-versus-one strategy is developed for classifying the sentiment orientations of online reviews into three categories: positive, neutral, and negative. In the second part, based on the percentages of the online reviews with different sentiment orientations and the numbers of online reviews of different products crawled from the website, an interval-valued intuitionistic fuzzy number is constructed to represent the performance of an alternative product with respect to the product attribute. Additionally, the interval-valued intuitionistic fuzzy TOPSIS method is employed to determine a ranking of the alternative products. Finally, a case analysis is provided to illustrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

Online product reviews have significant impacts on consumers’ purchase decisions. To support consumers’ purchase decisions, how to rank the products through online reviews is a valuable research topic, while research concerning this issue is still relatively scarce. This paper proposes a method based on the sentiment analysis technique and the intuitionistic fuzzy set theory to rank the products through online reviews. An algorithm based on sentiment dictionaries is developed to identify the positive, neutral or negative sentiment orientation on the alternative product concerning the product feature in each review. According to the identified positive, neutral and negative sentiment orientations, an intuitionistic fuzzy number is constructed for representing the performance of an alternative product concerning a product feature. The ranking of alternative products is determined by intuitionistic fuzzy weighted averaging (IFWA) operator and preference ranking organization methods for enrichment evaluations II (PROMETHEE II). A case study is given to illustrate the use of the proposed method. The comparisons and experiments are further conducted to illustrate the characteristics and advantages of the proposed method. Converting the identified positive, neutral and negative sentiment orientations into intuitionistic fuzzy numbers is a new idea for processing and fusing a large number of sentiment orientations of online reviews. Based on the proposed method, decision support system can be developed to support the consumers’ purchase decisions more conveniently. [ABSTRACT FROM AUTHOR]