[Purpose/significance] Online reviews contain commodity emotional attitude of users. Analysis of the relationship between the users'emotional tendency of commodity and the commodity sales has important practical and theoretical significance for users and businesses.[Method/process]This paper uses the LDA model to extract the theme features vocabulary of commodity. Then, it analyzes the vocabulary sentiment. And last, it searches the relationship between the sentiment value of commodity characteristics and the online sales ranking by using Multiple regression model.[Result/conclusion] The results show that the characteristics of the "quality" and the "shopping" experience were more closely watched by users. Andthere is a high correlation between their emotional evaluation and the sales rankings of goods.
Ruan Guangce
,
Zhou Jianchao
. Research on the Correlation Between Users' Sentiment Expression and Commodity Sales Ranking[J]. Library and Information Service, 2017
, 61(22)
: 13
-19
.
DOI: 10.13266/j.issn.0252-3116.2017.22.002
[1] SCHLOSSER A E. Can including pros and cons increase the helpfulness and persuasiveness of online reviews? the interactive effects of ratings and arguments[J]. Journal of consumer psychology, 2011, 21(3):226-239.
[2] DUAN W, GU B, WHINSTON AB. Informational cascades and software adoption on the Internet:an empirical investigation[J]. MIS quarterly, 2009, 33(1):23-48.
[3] CHEVALIER J A, MAYZLIN D. The effect of word of mouth on sales:online book reviews[J]. Journal of marketing research, 2006, 43(3):345-354.
[4] 史伟, 王洪伟, 何绍义,等.基于微博情感分析的电影票房预测研究[J].华中师范大学学报(自科版), 2015, 49(1):66-72.
[5] ZHU F, ZHANG X Q. Impact of online consumer reviews on sales:the moderating role of product and consumer characteristics[J]. Journal of marketing, 2010, 74(2):133-148.
[6] 李俊,陈黎,王亚强,等.面向电子商务网站的产品属性提取算法[J].小型微型计算机系统,2013,34(11):2477-2481.
[7] MORAES R, VALIATIJ F, GAVIAO NETO W P. Document level sentiment classification an empirical comparison between SVM and ANN[J]. Expert system with applications, 2013, 40(2):621-633.
[8] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. The journal of machine learning research, 2003, 3(3):993-1022.
[9] 阮光册.基于LDA的网络评论主题发现研究[J].情报杂志, 2014(3):161-164.
[10] 陈文涛, 张小明, 李舟军.构建微博用户兴趣模型的主题模型的分析[J].计算机科学, 2013, 40(4):127-130.
