Articles

The impact of emotions on the helpfulness of movie reviews

 

R. Ullah^a, A. Zeb^a, W. Kim^a,b*

 

^a Graduate School of Culture Technology, KAIST, Yuseong-gu, Daejeon, South Korea.

^b Department of Business and Technology Management, KAIST, Yuseong-gu, Daejeon, South Korea. *Corresponding author. E-mail address: wonjoon.kim@kaist.edu

 

Abstract

Online customer reviews have become a significant source of product-related information for consumers. As a result of the growing number of customer reviews, determining which customer reviews are the most helpful is important in reducing information overload. The ways in which reviews can be helpful need to be identified. In this study, we examine the impact of emotional content in online customer reviews on the number of votes those customer reviews receive that indicate they were helpful. We find that content that is more emotional yields more votes. Furthermore, our findings suggest that reviews with positive emotional content have a positive effect on review helpfulness whereas reviews with negative emotional content have no effect on review helpfulness. This study contributes to an understanding of emotional content in word of mouth and has important implications for online retailers and consumers.

Keywords: Natural language processing; Online reviews; Review helpfulness; Word of mouth.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Fundings

This work was supported by the National Research Foundation of Korea Grant funded by the Korean government (NRF-2012-S1A3A-2033860).

 

References

Anderson, E.W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1, 5-17.         [ Links ]

Balasubramanian, S., & Mahajan, V. (2001). The Economic Leverage of the Virtual Community. International Journal of Electronic Commerce, 5, 103-138.         [ Links ]

Berger, J., & Milkman, K.L. (2012). What makes online content viral? Journal of Marketing Research, 49, 192-205.         [ Links ]

Buttle, F.A. (1998). Word of mouth: understanding and managing referral marketing. Journal of Strategic Marketing, 6, 241-254.         [ Links ]

Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach. Decision Support Systems, 50, 511-521.         [ Links ]

Chen, P.Y., Dhanasobhon, S., & Smith, M.D. (2008). All reviews are not equal: The disaggregate impact of reviews and reviewers at Amazon.com. Available at http://ssrn.com/abstract=918083         [ Links ]

Chen, P.Y., Wu, S.Y., & Yoon, J. (2004). The Impact of Online Recommendations and Consumer Feedback on Sales. AIS Electronic Library (AISeL).         [ Links ]

Chevalier, J.A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 345-354.         [ Links ]

Dellarocas, C., & Narayan, R. (2006). A statistical measure of a population's propensity to engage in post-purchase online word-of-mouth. Statistical Science, 21, 277-285.         [ Links ]

Derbaix, C., & Vanhamme, J. (2003). Inducing word-of-mouth by eliciting surprise-a pilot investigation. Journal of Economic Psychology, 24, 99-116.         [ Links ]

Duan, W., Gu, B., & Whinston, A.B. (2008). Do online reviews matter?-An empirical investigation of panel data. Decision Support Systems, 45, 1007-1016.         [ Links ]

Ghose, A., & Ipeirotis, P.G. (2010). Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer. Washington, DC: IEEE Trans. Knowledge Data Engrg., IEEE Computer Society.         [ Links ]

Ghose, A., Ipeirotis, P.G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31, 493-520.         [ Links ]

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23, 545-560.         [ Links ]

Greene, W.H. (2002). Econometric Analysis. Uppler Saddle, New Jersey: Prentice Hall.         [ Links ]

Hansen, L.K., Arvidsson, A., Nielsen, F.Å, Colleoni, E., & Etter, M. (2011). Good friends, bad news-affect and virality in twitter. In: Future information technology (pp. 34-43). Berlin-Heidelberg: Springer.         [ Links ]

Hennig-Thurau, T., Gwinner, K.P., Walsh, G., & Gremler, D.D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18, 38-52.         [ Links ]

Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15, 194-210.         [ Links ]

Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11, 205-217.         [ Links ]

Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70, 74-89.         [ Links ]

Mudambi, S.M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34, 185-200.         [ Links ]

Neelamegham, R., & Chintagunta, P. (1999). A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science, 18, 115-136.         [ Links ]

Raghunathan, R., Pham, M.T., & Corfman, K.P. (2006). Informational properties of anxiety and sadness, and displaced coping. Journal of Consumer Research, 32, 596-601.         [ Links ]

Richins, M.L. (1997). Measuring emotions in the consumption experience. Journal of Consumer Research, 24, 127-146.         [ Links ]

Rimé, B., Finkenauer, C., Luminet, O., Zech, E., & Philippot, P. (1998). Social sharing of emotion: New evidence and new questions. European Review of Social Psychology, 9, 145-189.         [ Links ]

Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems, 55, 863-870.         [ Links ]

Shiv, B., Loewenstein, G., & Bechara, A. (2005). The dark side of emotion in decision-making: When individuals with decreased emotional reactions make more advantageous decisions. Cognitive Brain Research, 23, 85-92.         [ Links ]

Siering, M., & Muntermann, J. (2013). What Drives the Helpfulness of Online Product Reviews? From Stars to Facts and Emotions. 11th International Conference on Wirtschaftsinformatik, Leipzig, Germany.         [ Links ]

Sundaram, D.S., Mitra, K., & Webster, C. (1998). Word-of-mouth communications: A motivational analysis. Advances in Consumer Research, 25, 527-531.         [ Links ]

Tiedens, L.Z., & Linton, S. (2001). Judgment under emotional certainty and uncertainty: the effects of specific emotions on information processing. Journal of Personality and Social Psychology, 81, 973-998.         [ Links ]

Toutanova, K., Klein, D., Manning, C.D., & Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1 (pp. 173-180). Association for Computational Linguistics.         [ Links ]

Yin, D., Bond, S., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38, 539-560.         [ Links ]

Zheng, Z., Wu, X., & Srihari, R. (2004). Feature selection for text categorization on imbalanced data. ACM SIGKDD Explorations Newsletter, 6, 80-89.         [ Links ]