Abstract
Online reviews are a critical component of the retail business ecosystem today. They help consumers share feedback and readers make informed choices. As such, it is important to understand the mechanism driving the creation of reviews and identify factors which make them useful for readers. Extant work in this field has largely ignored the distribution of thematic content in reviews and its role in review diagnosticity. This article attempts to bridge the gap. A novel approach is proposed to explore the distribution of thematic content in reviews, in terms of underlying topics, and test its impact on influence of reviews. The approach is illustrated through a case study using data from Yelp. Implications of the study for theory and practice are discussed.
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Notes
In the output of the LDA with 2 clusters, one of the topic clusters represented food related topics, while the other cluster was found to be a combination of service and ambience related topics. In the output of the LDA with 4 clusters, the first topic cluster represented food and the second cluster represented ambience. The third and fourth topic clusters represented service-related topics. As such, the output of the LDA with 3 clusters, where the three clusters represented food, service, and ambience respectively, was found to be most interpretable and ideal for further analysis.
References
Anderson, M. (2014). 88% of consumers trust online reviews as much as personal recommendations. Retrieved July 6, 2018, from https://searchengineland.com/88-consumers-trust-online-reviews-much-personal-recommendations-195803
Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17, 99–126. https://doi.org/10.2753/JEC1086-4415170204.
Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17–26. https://doi.org/10.1016/j.dss.2017.01.006.
Bearde, W. O., Ingram, T. N., & Raymond, L. (2007). Marketing. McGraw-Hill/Irwin: Principles and Perspectives.
Blei, D. M., Edu, B. B., Ng, A. Y., Edu, A. S., Jordan, M. I., & Edu, J. B. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993.
Buettner, R. (2017). Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electronic Markets, 27(3), 247–265. https://doi.org/10.1007/s12525-016-0228-z.
Chen, Z., & Lurie, N. H. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463–476. https://doi.org/10.1509/jmr.12.0063.
Corley, J. K., Jourdan, Z., & Ingram, W. R. (2013). Internet marketing: A content analysis of the research. Electronic Markets, 23(3), 177–204. https://doi.org/10.1007/s12525-012-0118-y.
Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 7–35. https://doi.org/10.17705/1CAIS.03907.
DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics., 41(6), 520–906. https://doi.org/10.1016/j.poetic.2013.08.004.
Dimoka, A., Hong, Y., & Pavlou, P. A. (2012). On product uncertainty in online markets: Theory and evidence. MIS Quarterly, 36(2), 395–426. https://doi.org/10.2307/41703461.
Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K texual disclosure: Evidence from latent Dirichlet allocation. Journal of Accounting and Economics, 64(2–3), 221–245. https://doi.org/10.1016/j.jacceco.2017.07.002.
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x.
Evans, J. R., & Berman, B. (2002). Marketing: Marketing in the 21st century. Atomic Dog Pub Inc.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313. https://doi.org/10.1287/isre.1080.0193.
Gale, T. (2007). Goods and services. Retrieved from https://www.encyclopedia.com/finance/finance-and-accounting-magazines/goods-and-services
Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512. https://doi.org/10.1109/TKDE.2010.188.
Goes, P. B., Lin, M., & Yeung, C. m. A. (2014). “Popularity effect” in user-generated content: Evidence from online product reviews. Information Systems Research, 25(2), 222–238. https://doi.org/10.1287/isre.2013.0512.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009.
Huang, L., Tan, C.-H., Ke, W., & Wei, K.-K. (2013). Comprehension and assessment of product reviews: A review-product congruity proposition. Journal of Management Information Systems, 30(3), 311–343. https://doi.org/10.2753/MIS0742-1222300311.
Jiang, Z., & Benbasat, I. (2004). Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems, 21(3), 111–147. https://doi.org/10.1080/07421222.2004.11045817.
Kempf, D. S., & Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325–338. https://doi.org/10.2307/3152031.
Kuan, K. K. Y., Hui, K.-L., Prasarnphanich, P., & Lai, H.-Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 48–71. https://doi.org/10.17705/1jais.00386.
Lee, S., & Choeh, J. Y. (2016). The determinants of helpfulness of online reviews. Behaviour and Information Technology, 35(10), 853–863. https://doi.org/10.1080/0144929X.2016.1173099.
Lovelock, C. H. (1983). Classifying services to gain strategic marketing insights. Journal of Marketing, 47(3), 9–20. https://doi.org/10.2307/1251193.
Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and yelp review fraud. Management Science, 62(12), 3412–3427. https://doi.org/10.1287/mnsc.2015.2304.
Lugmayr, A., & Grueblbauer, J. (2017). Review of information systems research for media industry–recent advances, challenges, and introduction of information systems research in the media industry. Electronic Markets, 27(1), 33–47. https://doi.org/10.1007/s12525-016-0239-9.
Ma, B., Zhang, D., Yan, Z., & Kim, T. (2013). An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. Journal of Electronic Commerce Research, 14(4), 304–314.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
Otterbacher, J., & Arbor, A. (2009). “Helpfulness” in online communities: A measure of message quality. Proceedings of the 27th International Conference on Human Factors in Computing Systems - CHI ‘09, 955–964. https://doi.org/10.1145/1518701.1518848
Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87, 598–612. https://doi.org/10.1016/j.jretai.2011.05.002.
Pavlou, Liang, & Xue. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136. https://doi.org/10.2307/25148783.
Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). LIWC2007: Linguistic inquiry and word count.
Podium (2018). 2017 state of online reviews. Retrieved from http://learn.podium.com/rs/841-BRM-380/images/2017-SOOR-Infographic.jpg
Robinson, D., & Silge, J. (2017). Text mining with R. O’Reilly Media.
Schmiedel, T., Müller, O., & vom Brocke, J. (2018). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods. https://doi.org/10.1177/1094428118773858.
Shi, Z. M., Lee, G. M., & Whinston, A. B. (2016). Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Quarterly, 40(4), 1035–1056. 10.1145. https://doi.org/10.25300/MISQ/2016/40.4.11.
Steenkamp, J. B. E. M. (1990). Conceptual model of the quality perception process. Journal of Business Research, 21(4), 309–333. https://doi.org/10.1016/0148-2963(90)90019-A .
Vallurupalli, V., & Bose, I. (2017). Temporal changes in the impact of drivers of online review influence. In Proceedings of the 28th Australasian Conference on Information Systems.
Wallach, H. (2006). Topic modeling: Beyond bag-of-words. In Proceedings of the 23rd International Conference on Machine Learning, ACM (pp. 977–984).
Wan, Y. (2015). The Matthew effect in social commerce. Electronic Markets, 25(4), 313–324. https://doi.org/10.1007/s12525-015-0186-x .
Weathers, D., Swain, S. D., & Grover, V. (2015). Can online product reviews be more helpful? Examining characteristics of information content by product type. Decision Support Systems, 79, 12–23. https://doi.org/10.1016/j.dss.2015.07.009 .
Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology and Marketing, 30, 971–984. https://doi.org/10.1002/mar.20660 .
Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems, 37, 186–195. https://doi.org/10.1016/j.knosys.2012.08.003 .
Yelp (2018). Yelp Dataset Challenge. Retrieved May 1, 2018, from https://www.yelp.com/dataset/challenge
Yin, D., Bond, S., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.
Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1985). Problems and strategies in services marketing. Journal of Marketing, 49(2), 33–46. https://doi.org/10.2307/1251563 .
Zhang, Y., Jin, R., & Zhou, Z. H. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1–4), 43–52. https://doi.org/10.1007/s13042-010-0001-0 .
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Appendix
Appendix
Some sample reviews containing words with highest value of beta for different topics and highest value of beta spread between different pairs of topics have been presented below. It may be noted that specific terms and not entire reviews were assigned to different topic clusters. We have listed reviews in the appendix only to highlight the reviews containing considerable proportion of terms in the three identified topic clusters.
Topic 1 – Generic
“Very satisfied! Great food ! Quiet! Perfect! New to Fountain Hills. This is the place we were looking for”.
“Atmosphere is cool and laid back, nice drink menu, friendly staff! Just moved to the neighborhood and really like the place …”.
“Amazing authentic food . I highly recommend the buffet. Friendly staff . Definitely coming back.”
“ Staff is great, food is great, beer selection is super great. They have PBR in a can every day all day for $1.25.”
“Im in town for business for a few days. This place is close to my hotel so I popped in to try it. \n\nFantastic gyros, friendly service and reasonably priced . \n\nDont forget to pick up some baklava!
Topic 2 – Service
“… This time the service was even more slow. 15 minutes passed before we even got our water. 45 minutes later our food still hadnt arrived. The waitress kept speeding by our table and ignoring us …”.
“… At this point I look around the restaurant and I see some lady customer get up from her table, walks over to a waitress and screams at her saying shes walking out because her family too was still waiting for their food after over an hour. Within 15 minutes like a trickle effect I watch 3 more tables walk out havent even gotten their food yet either …”.
“… Aside from the layout problems with the lines, the line did move fairly quickly and we were able to place our orders in a fair amount of time. After we placed our orders we sat down at one of the tables and waited. While waiting we noticed more layout issues. The spot where the orders come up is on the opposite side of the store from the tables, so if you are sitting down its difficult to hear if they are calling your number …”.
“… We waited 10 min before anyone noticed us. A waiter came over and took drink orders, which we received quickly, but no sugar or silverware. We were ready to order but could not get the waiters attention …”.
“… At the bar we waited an unreasonable amont of time to be served. The bartender completely ignored us and only went to people who yelled for him or frantically waived their arms signaling him over.\n\nAfter a long wait I ordered a long Island Iced Tea …”
Topic 3 – Food
“… My husband had the Chefs Taco Tasting Platter. It was five different tacos with two salsas. I didnt taste the tacos but my husband said he couldnt taste what taco was chicken from the beef and pork and liked the vegetable tacos best. (that is very unusual for him) He also said they had too much raw cabbage in them for his taste …”.
“… Our entrees were all excellent. Salmon, Short ribs and steak. The Salmon had this awesome crispy skin, delicious blue cheese potatoes. Steak was excellent. Short ribs had a cumin citrus sauce that was so good …”.
“… For my meal I had the National Dish of Malaysia the Ri Nasi Lemak. It was a pile of rice with fried chicken, hard boiled egg, cucumbers, and spicy chili anchovy sauce. The chicken was tasty and seemed infused with mild flavors …”.
“… This Puerto Rican inspired eatery, which is excellent for small groups and sharing, also has some heartier dishes on its menu such as the Pork Pernil or Ropa Vieja, which are a 12 hour slow cooked pork and beef pot roast …”.
“… Basically it was a grilled chicken salad. The salad was made with crisp fresh greens, canned mandarins, LOTs of Carrots and Lots of crisp tortilla strips. The chicken on it was very tasty but slightly dry. I thought the cashews were very good they seemed real sweet but had a spicy kick at the end and the ginger dressing was good too. Overall it was a nice salad that I would order again but would ask for them to go lighter on the tortilla strips …
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Vallurupalli, V., Bose, I. Exploring thematic composition of online reviews: A topic modeling approach. Electron Markets 30, 791–804 (2020). https://doi.org/10.1007/s12525-020-00397-5
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DOI: https://doi.org/10.1007/s12525-020-00397-5