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.

中文翻译：

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使用基于深度学习的意见对挖掘和 q 梯级正畸对模糊交互 Heronian 均值算子的在线购物决策算法

消费者在网购过程中，通常会比较同一商品在不同电商平台上的评论信息。来自不同平台的在线评论的情感取向交互影响消费者的购买决策。然而，由于人工处理信息能力的限制，消费者难以准确地一一识别所有评论的情感倾向并描述其交互影响的过程。为此，我们提出了一种使用基于深度学习的意见挖掘和 q-rung orthopair 模糊交互加权 Heronian 均值 (q-ROFIWHM) 算子的在线购物支持模型。首先，在所提出的方法中，深度学习模型用于从在线评论中自动提取不同的产品属性词和观点词，并匹配相应的属性-意见对；同时，使用情感词典计算情感取向，包括正面、负面和中性情感。其次，计算三种情感对同一产品的每个属性的比例。根据不同平台属性情感的比例值，将情感信息转化为多个交叉决策矩阵，用q-rung orthopair模糊集表示。第三，考虑决策矩阵的交互特性，提出q-ROFIWHM算子聚合这种交叉决策信息，然后通过score函数确定排序结果，以支持消费者的购买决策。最后，