Data-driven product ranking: A hybrid ranking approach

Abstract

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers’ own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria; 2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score; 3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced.