东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (1): 143-147.DOI: 10.12068/j.issn.1005-3026.2017.01.029

• 管理科学 • 上一篇    下一篇

基于在线评论的产品选择方法

梁霞, 姜艳萍, 高梦   

  1. (东北大学 工商管理学院, 辽宁 沈阳110169)
  • 收稿日期:2015-08-06 修回日期:2015-08-06 出版日期:2017-01-15 发布日期:2017-01-13
  • 通讯作者: 梁霞
  • 作者简介:梁霞(1986-),女,山东济南人,东北大学博士研究生; 姜艳萍(1968-), 女, 辽宁沈阳人, 东北大学教授, 博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(71271050, 71571040).

Product Selection Methods Based on Online Reviews

LIANG Xia, JIANG Yan-ping, GAO Meng   

  1. School of Business Administration,Northeastern University,Shenyang 110169, China.
  • Received:2015-08-06 Revised:2015-08-06 Online:2017-01-15 Published:2017-01-13
  • Contact: JIANG Yan-ping
  • About author:-
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摘要: 随着大数据时代的到来,电子商务网站规模迅速扩大,产品的种类和数量已成海量规模,消费者需要高效的产品选择方法帮助他们做出决策.为了提出基于在线评论的产品选择方法,首先,确定在线评论的效用,并对产品属性进行提取得到属性集合,在考虑评论效用的情况下确定属性权重;然后,对在线评论中的情感词进行分析,将消费者情感倾向表示为关于评价标度的概率分布;再依据随机占优准则得到两两产品关于每个属性的占优关系;进一步,通过PROMETHEE II方法对备选产品进行排序.最后,通过一个产品选择的实例说明该方法的可行性和实用性.

关键词: 产品选择, 在线评论, 属性提取, 情感分析, 随机占优

Abstract: With the advent of the big data era, the scale of e-commerce platforms has expanded rapidly and consumers need more efficient product selection methods, which would help make decisions quickly in the massive kinds and a number of products. Therefore, a method of product selection based on online reviews is proposed. First, the utility values of the online reviews are calculated. By extracting the product attributes, the attribute set for product selection is obtained. Considering the utility values of the online reviews, the weight vector of attributes is determined. Second, by analyzing the sentiment words, consumers’ sentiment preferences are expressed in the format of probability distribution about sentiment levels. Based on the principle of stochastic dominance, the dominance relationships of any pairwise products on each attribute are determined. Third, by using PROMETHEE II, the ranking result of alternative products is obtained. Finally, an example of product selection is given to illustrate the feasibility and practicability of the proposed method.

Key words: product selection, online review, attribute extraction, sentiment analysis, stochastic dominance

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