The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers’ online shopping decision making and also served as a reference for product attribute performance improvement. This article proposes a sentiment-driven fuzzy cloud multicriteria model for online product ranking and performance to provide purchase recommendations. In this novel model, bidirectional long short-term memory network-conditional random fields, sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on the reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross combined and transformed into a type of q-ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.

Supplier selection is vital for enterprises to operate stably and achieve and sustain a competitive advantage. However, from the initial establishment to the gradual development and maturity of the enterprise, the supplier selection criteria change dynamically, and decision-makers are hardly in agreement in each stage, which creates challenges for enterprises when choosing suitable suppliers. As such, this paper proposes a multicriteria group decision-making method based on an interaction and feedback mechanism (IFM) and q-rung orthopair fuzzy sets theory. We introduce an IFM to achieve consensus among different decision-makers. We then develop the q-rung orthopair fuzzy weighted partitioned Bonferroni mean (q-ROFWPBM) operator to address the aggregation problem of dynamic multicriteria. A group decision-making algorithm combining the IFM and q-ROFWPBM operator is proposed to analyze the Hongxing Erke supplier selection. The results show that the proposed method can not only account for large differences of opinion among decision-makers during group decision-making but also consider the dynamic changes of supplier selection criteria in different stages of enterprise development and help enterprises choose suppliers suitable for their own development characteristics.

Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error-based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

In the process of online shopping, consumers usually compare the review information of the same product in different e-commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep-learning–based opinion mining and q-rung orthopair fuzzy interaction weighted Heronian mean (q-ROFIWHM) operators. First, in the proposed method, the deep-learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute-opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross-decision matrices, which are represented by the q-rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q-ROFIWHM operators are proposed to aggregate this cross-decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.