| 一种使用在线评论信息的商品购买决策分析方法 |
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| 引用本文： | 李永海. 一种使用在线评论信息的商品购买决策分析方法[J]. 运筹与管理, 2018, 27(2): 32-37. DOI: 10.12005/orms.2018.0032 |
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| 作者姓名： | 李永海 |
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| 作者单位： | 河南工业大学 管理学院,河南 郑州 450001 |
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| 基金项目： | 国家自然科学基金资助项目(71501063,71603073,71602051);河南省省属高校基本科研业务费专项资金资助项目(2014SBS003);河南省高校人文社科重点研究基地物流研究中心资助项目(2015-JD-04) |
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| 摘 要： | 在线评论信息对消费者的商品购买决策影响非常显著,如何使用数据体量较大的在线评论信息来进行有针对性的商品购买决策分析是近年来尤为需要关注的一个新研究内容。本文提出一种使用在线评论信息的商品购买决策分析方法。首先,通过在线评论信息的挖掘来确定了消费者所重点关注的关于候选商品的商品特征及其权重;然后,通过消费者情感的分析来构建了关于各候选商品的商品购买决策矩阵;在此基础上,通过给出的一种基于随机TOPSIS的方案排序方法来进行了各候选商品的排序。最后,依据携程网提供的关于三家客栈的在线评论信息进行了数据实验,从而说明了本文提出方法的实用性与可行性。
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| 关 键 词： | 在线评论信息 商品购买决策 特征挖掘 情感分析 离散概率分布 |
| 收稿时间： | 2016-09-06 |
A Decision Analysis Method for Product Purchase Based on Online Review Information |
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| LI Yong hai. A Decision Analysis Method for Product Purchase Based on Online Review Information[J]. Operations Research and Management Science, 2018, 27(2): 32-37. DOI: 10.12005/orms.2018.0032 |
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| Authors: | LI Yong hai |
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| Affiliation: | School of Management, Henan University of Technology, Zhengzhou 450001, China |
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| Abstract: | Online review information has an important impact on consumers’ product purchase decisions. In recent years, it has been especially needed to attach more attention to the new research on how to make a pertinent product purchase decision based on volume related online review information. The objective of this paper is to propose a decision analysis method for product purchase based on online review information. For the candidate products, their features that the customers focus on, as well as feature weights are determined through the mining of volume related online review information firstly. Secondly, the product purchase decision matrix concerning the candidate products is constructed through the consumers’ sentiment analysis. On the basis of this, the ranking result of all candidate products is determined through a given alternative ranking method which is based on stochastic TOPSIS. Finally, a data experiment, which is conducted using the online review information of three inns from the website ‘ctrip.com’, is used to illustrate the practicability and feasibility of the proposed method. |
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| Keywords: | online review information product purchase decision feature mining sentiment analysis discrete probability distribution |
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