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The Effects of Sentiment and Readability on Useful Votes for Customer Reviews with Count Type Review Usefulness Index
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 Title & Authors
The Effects of Sentiment and Readability on Useful Votes for Customer Reviews with Count Type Review Usefulness Index
Cruz, Ruth Angelie; Lee, Hong Joo;
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Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. uses a ratio-type helpfulness, while uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as In this study,`s count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes.`s online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store`s reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.
Online review;Sentiments;Readability;Count-type;Usefulness index;
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중립도 기반 선택적 단어 제거를 통한 유용 리뷰 분류 정확도 향상 방안,이민식;이홍주;

지능정보연구 , 2016. vol.22. 3, pp.129-142 crossref(new window)
Increasing Accuracy of Classifying Useful Reviews by Removing Neutral Terms, Journal of Intelligence and Information Systems, 2016, 22, 3, 129  crossref(new windwow)
Baccianella, S., A. Esuli, and F. Sehastiani, "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," Proceedings of the Seventh Conference on International Language Resources and Evaluation, (2010), 2200-2204.

Baek, H., J. Ahn, and Y. Choi, "Helpfulness of online consumer reviews: Readers' objectives and review cues," International Journal of Electronic Commerce, Vol.17, No.2(2012-13), 99-126.

Baum, S. and M. Spann, "The Interplay Between Online Consumer Reviews and Recommender Systems: An Experimental Analysis," International Journal of Electronic Commerce, Vol.19, No.1(2014), 129-162. crossref(new window)

Bickart, B. and R. M. Schindler, "Internet forums as influential sources of consumer information," Journal of Interactive Marketing, Vol.15, No.3(2001), 31-40. crossref(new window)

Chae, S. H., J. Im, and J. Y. Kang, "A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce," Journal of Intelligence and Information Systems, Vol. 21, No.4(2015), 53-77.

Chen, P., S. Dhanasobhon, and M. Smith, All Reviews Are Not Created Equal: The Disaggregate Impact of Reviews on Sales on, Working paper, Carnegie Mellon University, 2008, Available at SSRN: (Downloaded 1 November, 2015).

Chevalier, J. and D. Mayzlin, "The Effect of Word of Mouth on Sales: Online Book Reviews," Journal of Marketing Research, Vol.43, No.3(2006), 345-354. crossref(new window)

Choi, J. W. and H. J. Lee, "The Effects of Customer Product Review on Social Presence in Personalized Recommender Systems," Journal of Intelligence and Information Systems, Vol.17, No.3(2011), 115-130.

Chun, B. G. and H. C. Ahn, "A Collaborative Filtering System Combined with Users' Review Mining : Application to the Recommendation of Smartphone Apps," Journal of Intelligence and Information Systems, Vol.21, No.2(2015), 1-18.

Clemons, E., G. Gao, and L. Hitt, "When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry," Journal of Management Information Systems, Vol.23, No.2(2006), 149-171. crossref(new window)

Crowley, A. E. and W. D. Hoyer, "An Integrative Framework for Understanding Two-Sided Persuasion," Journal of Consumer Research, Vol.20, No.4(1994), 561-574. crossref(new window)

Dabholkar, P., "Factors Influencing Consumer Choice of a 'Rating Web Site': An Experimental Investigation of an Online Interactive Decision Aid," Journal of Marketing Theory and Practice Vol.14, No.4(2006), 259-273. crossref(new window)

Forman, C., A. Ghose, and B. Wiesenfeld, "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, Vol.19, No.3(2008), 291-313. crossref(new window)

Ghose, A. and P. G. Ipeirotis, "Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics," IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.10(2011), 1498-1512. crossref(new window)

Ghose, A. and P. G. Ipeirotis, "Designing Ranking Systems for Consumer Reviews: The Impact of Review Subjectivity on Product Sales and Review Quality," Proceedings of the 16th Annual Workshop on Information Technology and Systems, (2006).

Harris, R. B. and D. Paradice, "An investigation of the computer-mediated communication of emotions," Journal of Applied Sciences Research, Vol.3, No.12(2007), 2081-2090.

Hausman, J., B. H. Hall, and Z. Griliches, "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Vol.52, No.4(1984), 909-937. crossref(new window)

Jiang, Z. and I. Benbasat, "Investigating the influence of the functional mechanisms of online product presentations," Information Systems Research, Vol.18, No.4(2007), 454-470. crossref(new window)

Jiang, B. J. and K. Srinivasan, "Pricing and Persuasive Advertising in a Differentiated Market," Marketing Letters, forthcoming (2015), 1-10.

Korfiatis, N., E. Garcia-Bariocanal, and S. Sanchez-Alonso, "Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content," Electronic Commerce Research and Applications, Vol.11, No.3(2012), 205-217. crossref(new window)

Krosnick, J. A., D. S. Boninger, Y. C. Chuang, M. K. Berent, and C. G. Camot, "Attitude Strength: One Construct or Many Related Constructs?," Journal of Personality and Social Psychology, Vol.65, No.6(1993), 1132-1151. crossref(new window)

Kumar, N. and I. Benbasat, "The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, Vol.17, No.4(2006), 425-439. crossref(new window)

Michalke, M., "koRpus - ein R-paket zur textanalyse," Tagung experimentell arbeitender Psychologen(TeaP), (2012).

Mudambi, S. M. and D. Schuff, "What Makes a Helpful Online Review? A Study of Customer Reviews on," MIS Quarterly, Vol.34, No.1(2010), 185-200. crossref(new window)

Park, D. H. and S. Kim, "The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews," Electronic Commerce Research and Applications, Vol.7, No.4(2009), 399-410.

Park, D. H., J. Lee, and I. Han, "The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement," International Journal of Electronic Commerce, Vol.11, No.4(2007), 125-148. crossref(new window)

Pavlou, P. and A. Dimoka, "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, Vol.17, No.4(2006), 392-414. crossref(new window)

Schlosser, A., "Source Perceptions and the Persuasiveness of Internet Word-of-Mouth Communication," in Advances in Consumer Research (32), G. Menon and A. Rao (eds.), Duluth, MN: Association for Consumer Research, 2005, 202-203.

Shen, W., J. H. Yu, and J. Rees, "Competing for Attention: An Empirical Study of Online Reviewers' Strategic Behaviors," MIS Quarterly, Vol.39, No.3(2015), 683-696. crossref(new window)

Resnick, P., R. Zeckhauser, E. Friedman, and K. Kuwabara, "Reputation Systems," Communications of the ACM, Vol.43, No.12(2000), 45-48.

Riordan, M. A. and R. J. Kreuz, "Emotion encoding and interpretation in computer-mediated communication: Reasons for use," Computers in Human Behavior, Vol.26, No.6(2010), 1667-1673. crossref(new window)

Schindler, R. M. and B. Bickart, "Published word-of-mouth: Referable, consumer-generated information on the Internet," In C.P. Haugtvedt, K.A. Machleit, and R.F. Yalch (eds.), Online Consumer Psychology: Understanding and Influencing Behavior in the Virtual World, Hillsdale, NJ: Lawrence Erlbaum, (2005), 35-61.

Schindler, R. and B. Bickart, "Perceived helpfulness of online consumer reviews: The role of message content and style," Journal of Consumer Behaviour, Vol.11, No.3(2012), 234-243. crossref(new window)

Treisman, A., "Selective attention in man," British Medical Bulletin, Vol.20, No.1(1964), 12-16. crossref(new window)