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Abstract

Online reviews are crucial for evaluating product features and supporting consumers’ purchase decisions. However, as a result of online buying behaviors, consumer habits and discrete dynamic distribution characteristics of online reviews, consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers’ purchase decisions using the intuitionistic fuzzy scoring function and the “vertical projection distance” method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods.

Item Type:	Article
Authors/Creators:	Yang, Z and Xiong, G and Cao, Z and Li, Y and Huang, L
Keywords:	feature extraction, dictionaries, semantics, time series analysis, sentiment analysis, aggregates, decision making, fuzzy, sentiment orientation classification, DDIFWA operator, dynamic information preference, online purchase decision
Journal or Publication Title:	IEEE Access
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2169-3536
DOI / ID Number:	https://doi.org/10.1109/ACCESS.2019.2921403
Copyright Information:	Copyright 2019 IEEE.
Related URLs:	Google Scholar: Cao, Z
Item Statistics:	View statistics for this item

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