A Method of Product Selection Based on Online Reviews | Zendy

Xia Liang | Zendy; Jie Guo | Zendy; Yan Sun | Zendy; Xiaoxiao Liu | Zendy

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A Method of Product Selection Based on Online Reviews

Author(s) -

Xia Liang,

Jie Guo,

Yan Sun,

Xiaoxiao Liu

Publication year - 2021

Publication title -

mobile information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.346

H-Index - 34

eISSN - 1875-905X

pISSN - 1574-017X

DOI - 10.1155/2021/9656315

Subject(s) - computer science , ranking (information retrieval) , product (mathematics) , selection (genetic algorithm) , attributive , sentiment analysis , data mining , information retrieval , operations research , artificial intelligence , machine learning , linguistics , philosophy , geometry , mathematics , engineering

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.

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