摘要
在数字信息化时代,消费者在网上购物时越来越依赖在线评论,大数据爆炸式增长也导致消费者要花费大量的时间来阅读在线评论,筛选信息并做出决策。所以本研究旨在提出一个新的基于在线评论的决策支持框架,用于帮助消费者依据在线评论对可替代产品进行评估和选择。决策支持框架主要包括三个部分,1) 数据处理,用python抓取在线消费者评论进行数据清洗和预处理,提取出关键特征作为评价标准;2) 情感分析,利用朴素贝叶斯对在线评论进行情感分析,用积极意见的优势比作为模型的输出数据;3) 基准分析,利用RDEA模型来计算可替代产品的效率得分,根据效率得分进行排名。最后,对京东平台上爬取的15款笔记本电脑的在线评论进行实证分析,来验证所提出的决策支持框架有用性和适用性,并进行了对比分析,结果证明提出的方法更符合客观实际情况,并且步骤更简单,易于操作。
In the era of digital information, consumers increasingly rely on online reviews when shopping online. The explosive growth of big data also leads to consumers spending a lot of time reading online reviews, screening information, and making decisions. Therefore, this study aims to propose a new decision support framework based on online reviews to help consumers evaluate and select alternative products based on online reviews. The decision support framework mainly includes three parts: 1) Data processing, which uses python to capture online consumer reviews for data cleaning and preprocessing, and extracts key features as evaluation criteria;2) Emotional analysis, which uses naive Bayes to conduct emotional analysis on online reviews, and uses the advantage ratio of positive opinions as the output data of the model;3) Benchmark analysis, which uses RDEA model to calculate the efficiency score of alternative products, and rank according to the efficiency score. Finally, an empirical analysis is conducted from the online comments of 15 laptops crawled on the JD platform to verify the usefulness and applicability of the proposed decision support framework, and a comparative analysis is conducted. The results show that the proposed method is more in line with the objective reality, and the steps are simpler and easier to operate.
出处
《运筹与模糊学》
2023年第2期528-542,共15页
Operations Research and Fuzziology
