Probability theory and mathematical statistics

Probabilistic linguistic TODIM method for selecting products through online product reviews

Online product reviews significantly impact the online purchase decisions of consumers. However, extant decision support models have neglected the randomness and fuzziness of online reviews and the interrelationships among product features. This study presents an integrated decision support model that can help customers discover desirable products online. This proposed model encompasses three modules: information acquisition, information transformation, and integration model. We use the information acquisition module to gather linguistic intuitionistic fuzzy information in each review through sentiment analysis. We also apply the information transformation module to convert the linguistic intuitionistic fuzzy information into linguistic intuitionistic normal clouds (LINCs). The integration module is employed to obtain the overall LINCs for each product. A ranked list of alternative products is determined. A case study on Taobao.com is then provided to illustrate the effectiveness and feasibility of the proposal, along with sensitivity and comparison analyses, to verify its stability and superiority. Finally, conclusions and future research directions are suggested.

A Linguistic Intuitionistic Cloud Decision Support Model with Sentiment Analysis for Product Selection in E-commerce

Representing sentiment analysis results of online reviews using interval type-2 fuzzy numbers and its application to product ranking

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Online product ratings, as a type of electronic word-of-mouth, play an important role for helping consumers select desirable products, but it is difficult for consumers to read a large number of online ratings on e-commerce Web site. To support consumer’s purchase decision, how to rank the candidate products based on online product ratings and consumer’s preferences is a noteworthy research topic, while the existing studies concerning this issue are still relatively scarce. This paper proposes a method for ranking products based on online multi-attribute product ratings. In the method, a discrete percentage distribution of the evaluation of each candidate product with respect to each attribute based on online ratings is first constructed, and the $$3\sigma $$
 criterion is used to eliminate the anomalous ratings. Then, by defining of the stochastic dominance rules and the stochastic dominance degrees on comparing two discrete percentage distributions, the stochastic dominance relation between each pair of products is determined, and the corresponding stochastic dominance degree is calculated. Further, according to the obtained stochastic dominance degrees, the ranking of candidate products can be determined using the PROMETHEE-II method. A case study on selecting the automobile is given to illustrate the use of the proposed method.

Supporting consumer’s purchase decision: a method for ranking products based on online multi-attribute product ratings

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Supporting consumer’s purchase decision: a method for ranking products based on online multi-attribute product ratings