Examining Discrepancies between Online Product Ratings and Sentiments Expressed in Review Contents
DOI:
https://doi.org/10.22105/r4kfnt67Keywords:
Reputation system, Rating, review contents, sentiment, Semantic orientation approach (SOA)Abstract
The existing and most commonly used information systems and consumer studies on the functional aspect of 'reputation systems have two streams: the 'rating systems and the 'review content systems'. Both systems are offered by most of the popular e-commerce retailers to enhance customer communication experiences. However, there is limited research on the relationship between customers' rating scores and the sentiments expressed in their review contents, which significantly affects the reliability of the overall review in the reputation systems. A computational linguistics approach (Semantic orientation approach) using Amazon's (UK) product review data (34,621 reviews) is employed to unveil the gap between the numerical ratings and the sentiments of review contents from the two reputation systems. Results show that although customers give the same high rating for products that have a lower/higher overall rating, the customers' expressions of sentiment in the review content are actually less/more positive compared to products that have a higher/lower overall rating.
References
[1]Topic: E-commerce worldwide. (2024, May 16). Statista. https://www.statista.com/topics/871/online-shopping/#topicOverview
[2]Cheung, C. M. K., & Lee, M. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218–225. https://doi.org/10.1016/j.dss.2012.01.015
[3]Sardar, A., Manzoor, A., Shaikh, K. A., & Ali, L. (2021). An Empirical examination of the impact of EWOM information on young consumers' online purchase intention: Mediating role of eWOM Information Adoption. SAGE Open, 11(4), 215824402110525. https://doi.org/10.1177/21582440211052547
[4]Wahab, O. A., Bentahar, J., Otrok, H., & Mourad, A. (2015). A survey on trust and reputation models for Web services: Single, composite, and communities. Decision Support Systems, 74, 121–134. https://doi.org/10.1016/j.dss.2015.04.009
[5]Changchit, C., & Klaus, T. (2019). Determinants and impact of online reviews on product satisfaction. Journal of Internet Commerce, 19(1), 82–102. https://doi.org/10.1080/15332861.2019.1672135
[6]Chen, B., Guo, J. Q., Tseng, B. L., & Yang, J. (2011). User reputation in a comment rating environment. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2020408.2020439
[7]Karthik, R., & Ganapathy, S. (2021). A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce. Applied Soft Computing, 108, 107396. https://doi.org/10.1016/j.asoc.2021.107396
[8]Thoma, V., & Williams, A. (2013). The devil you know: The effect of brand recognition and product ratings on consumer choice. Judgment and Decision Making, 8(1), 34–44. https://doi.org/10.1017/s1930297500004484
[9]Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707. https://doi.org/10.1287/mnsc.1110.1458
[10]Resnick, P., & Zeckhauser, R. (2004). Trust among strangers in internet transactions: Empirical analysis of eBay's reputation system. In Emerald (MCB UP) eBooks (pp. 127–157). https://doi.org/10.1016/s0278-0984(02)11030-3
[11]Mishra, A., & Satish, S. (2016). EWOM: Extant Research Review and Future Research Avenues. Vikalpa, 41(3), 222–233. https://doi.org/10.1177/0256090916650952
[12]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(1), 38–52. https://doi.org/10.1002/dir.10073
[13]Wu, Y., Liu, T., Teng, L., Zhang, H., & Xie, C. (2021). The impact of online review variance of new products on consumer adoption intentions. Journal of Business Research, 136, 209–218. https://doi.org/10.1016/j.jbusres.2021.07.014
[14]Bornfeld, B., Rafaeli, S., & Raban, D. R. (2014). Electronic Word-of-Mouth Spread in Twitter as a Function of Message Sentiment. Proceedings of the Fourth International Conference on Social Eco-Informatics, 6–14. https://www.thinkmind.org/articles/sotics_2014_1_20_60011.pdf
[15]Kim, S., Kandampully, J., & Bilgihan, A. (2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, 243–254. https://doi.org/10.1016/j.chb.2017.11.015
[16]Liu, Y., & Keng, C. (2014). Cognitive dissonance, social comparison, and disseminating untruthful or negative truthful EWOM messages. Social Behavior and Personality, 42(6), 979–995. https://doi.org/10.2224/sbp.2014.42.6.979
[17]Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM). International Journal of Advertising, 28(3), 473–499. https://doi.org/10.2501/s0265048709200709
[18]Kang, M. G., Sun, B., Liang, T., & Mao, H. (2022). A study on the influence of online reviews of new products on consumers' purchase decisions: An empirical study on JD.com. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.983060
[19]Li, K., Chen, Y., & Zhang, L. (2020). Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category. Journal of Retailing and Consumer Services, 55, 102107. https://doi.org/10.1016/j.jretconser.2020.102107
[20]Winkielman, P., & Gogolushko, Y. (2018). Influence of suboptimally and optimally presented affective pictures and words on Consumption-Related behavior. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.02261
[21]Shan, G., Zhou, L., & Zhang, D. (2021). From conflicts and confusion to doubts: Examining review inconsistency for fake review detection. Decision Support Systems, 144, 113513. https://doi.org/10.1016/j.dss.2021.113513
[22]Babic-Rosario, A., Sotgiu, F., & De Valck, K. (2019). Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation. Journal of the Academy of Marketing Science, 48(3), 422–448. https://doi.org/10.1007/s11747-019-00706-1
[23]Calhoun, C., & Benhabib, S. (1994). Situating the self: gender, community and postmodernism in contemporary ethics. The Philosophical Review. https://doi.org/10.2307/2185801
[24]Huang, Z. H., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259. https://doi.org/10.1016/j.elerap.2012.12.003
[25]Ma, X., Khansa, L., Deng, Y., & Kim, S. S. (2013). Impact of prior reviews on the subsequent review process in reputation systems. Journal of Management Information Systems, 30(3), 279–310. https://doi.org/10.2753/mis0742-1222300310
[26]Gerber, J. P., Wheeler, L., & Suls, J. (2018). A social comparison theory meta-analysis 60+ years on. Psychological Bulletin, 144(2), 177–197. https://doi.org/10.1037/bul0000127
[27]Gruen, T. W., Osmonbekov, T. D., & Czaplewski, A. J. (2006). eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business Research, 59(4), 449–456. https://doi.org/10.1016/j.jbusres.2005.10.004
[28]Strack, F., & Mussweiler, T. (1997). Explaining the enigmatic anchoring effect: Mechanisms of selective accessibility. Journal of Personality and Social Psychology, 73(3), 437–446. https://doi.org/10.1037/0022-3514.73.3.437
[29]Locher, M. A., & Watts, R. J. (2005). Politeness theory and relational work. Journal of Politeness Research, 1(1). https://doi.org/10.1515/jplr.2005.1.1.9
[30]Rees, C. E., & Knight, L. (2008). Thinking 'no' but saying 'yes' to student presence in general practice consultations: politeness theory insights. Medical Education, 42(12), 1152–1154. https://doi.org/10.1111/j.1365-2923.2008.03173.x
[31]Panofsky, E. (2018). Studies in iconology. In Routledge eBooks. https://doi.org/10.4324/9780429497063
[32]Babin, L. A., & Burns, A. C. (1997). Effects of print ad pictures and copy containing instructions to imagine on mental imagery that mediates attitudes. Journal of Advertising, 26(3), 33–44. https://doi.org/10.1080/00913367.1997.10673527
[33]Yüce, P., & Highhouse, S. (1998). Effects of attribute set size and pay ambiguity on reactions to 'help wanted' advertisements. Journal of Organizational Behavior, 19(4), 337–352. https://doi.org/10.1002/(sici)1099-1379(199807)19:4
[34]Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Language Resources and Evaluation. http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf
[35]Devitt, A., & Ahmad, K. (2007). Sentiment Polarity Identification in Financial News: A cohesion-based approach. Meeting of the Association for Computational Linguistics, 984–991. http://www.tara.tcd.ie/bitstream/2262/25729/1/DevittAhmad_acl2007final.pdf
[36]Hung, C., & Lin, H. (2013). Using Objective Words in SentiWordNet to Improve Word-of-Mouth Sentiment Classification. IEEE Intelligent Systems, 28(2), 47–54. https://doi.org/10.1109/mis.2013.1
[37]Manning, C. D. (2011). Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics? In Lecture Notes in Computer Science (pp. 171–189). https://doi.org/10.1007/978-3-642-19400-9_14
[38]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. https://doi.org/10.3115/1073445.1073478
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