Abstract
With the rapid development of information technology and market economy, global e-commerce platform develops rapidly. Recently, online reviews are widely available on e-commerce platforms to express customers’ experience of products. When ranking alternative products based on online reviews, how to make full use of the information in online reviews to represent the sentiment analysis results of online reviews is an important prerequisite for decision analysis. To this end, we propose a method for measuring the time utility and support utility of online reviews. Then a method for representing the sentiment analysis results of online reviews in the form of linguistic distribution is proposed. In addition, in view of the attributes and their weights being unknown, we propose a method for extracting product attributes from online reviews by using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm; and the objective weights of attributes are determined through the Criteria Importance through Intercriteria Correlation (CRITIC) method. Additionally, in order to highlight the differences between the alternatives, the roulette wheel selection algorithm is first used to randomly select product attributes. Then the alternative products can be ranked by the extended Multi-Attributive Border Approximation area Comparison (MABAC) method with mixed information. Finally, we illustrate the applicability of the proposed method through a case study of selecting a 5G mobile phone and simulation experiment.
Highlights
With the continuous updating and iteration of network technology, information technology, and communication technology, we have gradually stepped into the era of big data
We propose a new method to rank alternative products on the basis of online reviews. e specific contributions of this paper are summarized as follows
This study overcomes the problem that the reference value of online reviews is not fully utilized in the existing research
Summary
With the continuous updating and iteration of network technology, information technology, and communication technology, we have gradually stepped into the era of big data. One is the analysis and processing of online reviews; another part is the product ranking method. With the continuous introduction of information forms, the sentiment analysis results for online reviews are gradually being expressed in the forms of fuzzy numbers [11, 12, 36], linguistic distribution (LD) [37], hesitant fuzzy sets (HFS) [38], and so forth. Judging from the existing research results, the research on sentiment analysis of online reviews and alternative products ranking has achieved preliminary results, there are still some problems that deserve further research. Most of the existing product selection methods based on online reviews set the product attributes and their weights in advance. Ere are fewer methods for product selection based on online reviews with unknown attributes and weights.
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