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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 16-27    DOI: 10.11925/infotech.2096-3467.2017.09.02
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Identifying Reviews with More Positive Votes——Case Study of Amazon.cn
Wu Jiang, Liu Wanwan
School of Information Management, Wuhan University, Wuhan 430072, China
The Center of E-commerce Research and Development of Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This article examines online reviews attracting more positive votes from consumers, aiming to identify those high quality reviews based on the information adoption and negative bias theories. [Methods] First, we retrieved 12 393 reviews on cellphones from Amazon.cn. Then, we investigated the impacts of the review’s characteristics on the numbers of positive votes with the help of zero inflated negative binomial regression and text analysis methods. The characteristics we studied include reviewer’s credibility, review’s quality and extremity. [Results] The usefulness of the reviewer’s previous posting, the information quality of the reviews, the number of comments, the extreme ratings, and the negative level of the reviews helped them receive more positive votes. However, the reviewers bought the products or not, and the number of the previously posted reviews had negative influence on the number of votes. [Limitations] Only investigated cellphones in this study. [Conclusions] This paper helps E-commerce websites improve their review ranking algorithms.

Key wordsOnline Review      Online Review Helpfulness      Review Vote     
Received: 23 May 2017      Published: 18 October 2017
ZTFLH:  G203  

Cite this article:

Wu Jiang,Liu Wanwan. Identifying Reviews with More Positive Votes——Case Study of Amazon.cn. Data Analysis and Knowledge Discovery, 2017, 1(9): 16-27.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.09.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I9/16

变量类型 变量名 变量 变量解释
自变量 评论者有用性 reUse 评论者获得有用性投票数/
评论者获得的总投票数
评论者发表
评论数
reNum 评论者已经发布的评论的
总数
是否确认购买 buyornot 评论者是否确认在亚马逊
上购买商品(0表示未确
认购买; 1表示确认购买)
评论信息量 reInfo 评论文本所包含信息量
评论回复数 comment 评论下的回复总数
评分极性 rateGap 评分与平均评分差值的绝
对值
评论积极倾向 pos 积极词汇总数/评论长度
评论消极倾向 neg 消极词汇总数/评论长度
因变量 评论有用
性投票数
usefulNum 评论获得的有用性投票
总数
类别 变量名 平均值 标准差 最大值 最小值
评论者信度 usefulNum 1.70 13.77 755.00 0.00
reUse 0.37 0.38 1.00 0.00
reNum 9.91 27.73 781.00 1.00
buyornot 0.97 0.17 1.00 0.00
评论信息质量 reInfo 0.29 0.25 1.00 0.00
comment 0.19 0.81 44.00 0.00
评论极性 rateGap 1.01 0.81 3.41 0.03
pos 0.02 0.03 0.31 0.00
neg 0.01 0.02 0.18 0.00
零膨胀因子 time 417.72 265.92 973.00 0.00
rank 34.20 20.01 97.00 1.00
comNum 652.26 2495.11 6011.00 100.00
reUse reNum buyornot reInfo comment rateGap pos neg comNum time rank
reUse 1.00 0.22 -0.06 0.01 0.08 0.07 0.00 0.02 -0.06 -0.01 -0.05
reNum 0.22 1.00 -0.05 0.00 0.00 -0.06 0.00 -0.01 0.02 0.00 0.02
buyornot -0.06 -0.05 1.00 0.02 -0.07 -0.10 0.02 -0.05 0.07 0.01 -0.06
reInfo 0.01 0.00 0.02 1.00 0.04 -0.06 -0.05 -0.04 -0.05 -0.02 0.01
comment 0.08 0.00 -0.07 0.04 1.00 0.08 -0.03 0.01 -0.13 -0.13 -0.07
rateGap 0.07 -0.06 -0.10 -0.06 0.08 1.00 -0.13 0.19 0.04 -0.01 0.08
pos 0.00 0.00 0.02 -0.05 -0.03 -0.13 1.00 -0.13 -0.08 -0.07 0.00
neg 0.02 -0.01 -0.05 -0.04 0.01 0.19 -0.13 1.00 0.00 0.00 0.02
comNum -0.06 0.02 0.07 -0.05 -0.13 0.04 -0.08 0.00 1.00 0.78 0.26
time -0.01 0.00 0.01 -0.02 -0.13 -0.01 -0.07 0.00 0.78 1.00 0.17
rank -0.05 0.02 -0.06 0.01 -0.07 0.08 0.00 0.02 0.26 0.17 1.00
模型1 模型2 模型3
系数 P值 系数 P值 系数 P值
(Intercept) -1.598 0.000 -1.576 0.000 -2.081 0.000
reUse 5.836 0.000 4.841 0.000 4.651 0.000
reNum -0.006 0.000 -0.005 0.000 -0.004 0.000
buyornot -1.445 0.000 -1.334 0.000 -1.152 0.000
reInfo 0.451 0.000 0.630 0.000
comment 0.476 0.000 0.441 0.000
rateGap 0.324 0.000
pos -2.136 0.001
neg 2.418 0.034
Log(theta) -0.961 0.000 -0.721 0.000 -0.640 0.000
零膨胀模型
(Intercept) -30.124 0.000 -30.445 0.000 -30.500 0.000
rank 0.043 0.000 0.0435 0.000 0.044 0.000
Log(time) 4.230 0.000 4.272 0.000 4.277 0.000
LogLike -12940 -12530 -12380
已发表评论数 比率 平均有用性投票数 评分差值(评分)
>1 63% 1.64 0.93(4.24)
1 37% 1.80 1.13(3.87)
是否真实购买 比率 平均有用性投票数 评分差值(评分)
1 97% 1.51 1.00(4.16)
0 3% 7.73 1.46(3.33)
类别 假设 结果
评论者
信度
H1a: 评论者有用性对评论有用投票数有显著的正向影响。 支持
H1b: 评论者发表的评论数对评论有用性投票数有显著的正向影响 不支持
H1c: 评论者的购买的真实性对评论有用性投票数有显著的正向影响。 不支持
评论
信息
质量
H2a: 评论信息量对评论有用性投票数有显著的正向影响。 支持
H2b: 评论回复数对评论有用性投票数有显著的正向影响。 支持
评论
极性
H3a: 评分极性对评论有用性投票数有显著的正向影响。 支持
H3b: 评论文本消极情感倾向对评论有用性投票数有正向影响。 支持
H3c: 评论文本积极情感倾向对评论有用性投票数有负向影响。 支持
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