Predicting the “helpfulness” of online consumer reviews

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• 作者：Jyoti Prakash Singh^a ; ^{jps@nitp.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Seda Irani^b ; ^{sedatuna@hotmail.com" class="auth_mail" title="E-mail the corresponding author} ; Nripendra P. Rana^b ; ^{nrananp@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Yogesh K. Dwivedi^b ; ^{ykdwivedi@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Sunil Saumya^a ; ^{sunils.cse15@nitp.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Pradeep Kumar Roy^a ; ^{pradeep.cse15@nitp.ac.in" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Online user reviews ; Helpfulness ; Product features ; Text mining ; Product ranking
• 刊名：Journal of Business Research
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：70
• 期：Complete
• 页码：346-355
• 全文大小：1007 K

文摘

Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.