A decision-making algorithm for online shopping using deep-learning-based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators

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.