Sentence-based text analysis for customer reviews

Marketing Science

Volumn 35, Issue 6, 2016, Pages 953-975

  Büschken, Joachim ^a   Allenby, Greg M ^b  

^a CATHOLIC UNIVERSITY OF EICHSTÄTT INGOLSTADT   (Germany)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

Bayesian analysis; Big data; Extended LDA model; Text data; Unstructured data; User generated content

Indexed keywords

EID: 84995685575     PISSN: 07322399     EISSN: 1526548X     Source Type: Journal    
DOI: 10.1287/mksc.2016.0993     Document Type: Review

References (34)

1. Andrzejewski D, Zhu X, Craven M (2009) Incorporating domain knowledge into topic modeling via Dirichlet forest priors. Proc. Internat. Conf. Machine Learn. 382(26):25–32.
2. Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.
3. Berger J, Sorensen AT, Rasmussen SJ (2010) Positive effects of negative publicity: When negative reviews increase sales. Marketing Sci. 29(5):815–827.
4. Blei DM, Lafferty JD (2006) Dynamic topic models. Proc. 23rd Internat. Conf. Machine Learn. (ACM, New York), 113–120.
5. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(January):993–1022.
6. Büschken J, Otter T, Allenby GM (2013) The dimensionality of customer satisfaction survey responses and implications for driver analysis. Marketing Sci. 32(4):533–553.
7. de Jong MG, Lehmann DR, Netzer O (2012) State-dependence effects in surveys. Marketing Sci. 31(5):838–853.
8. Dellarocas C, Zhang XM, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interactive Marketing 21(4):23–45.
9. DeSarbo WS, Lehmann DR, Hollman FG (2004) Modeling dynamic effects in repeated-measures experiments involving preference/ choice: An illustration involving stated preference analysis. Appl. Psych. Measurement 28(3):186–209.
10. Ding M, Grewal R, Liechty J (2005) Incentive-aligned conjoint analysis. J. Marketing Res. 42(1):67–82.
11. Fader PS, Hardie BGS, Huang CY (2004) A dynamic changepoint model for new product sales forecasting. Marketing Sci. 23(1): 50–65.
12. Frühwirth-Schnatter S (2006) Finite Mixture and Markov Switching Models (Springer ScienceCBusiness Media, New York).
13. Gal D, Rucker DD (2011) Answering the unasked question: Response substitution in consumer surveys. J. Marketing Res. 48(1):185–195.
14. Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.
15. Godes D, Mayzlin D (2004) Using online conversations to study word-of-mouth communication. Marketing Sci. 23(4): 545–560.
16. Gruber A, Weiss Y, Rosen-Zvi M (2007) Hidden topic Markov models. Internat. Conf. Artificial Intelligence Statist., 163–170.
17. Kamakura WA, Kim BD, Lee J (1996) Modeling preference and structural heterogeneity in consumer choice. Marketing Sci. 15(2):152–172.
18. Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.
19. Ludwig S, de Ruyter K, Friedman M, Brüggen EC, Wetzels M, Pfann G (2013) More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. J. Marketing 77(1):87–103.
20. McCallum A, Corrada-Emmanuel A, Wang X (2005) The authorrecipient- topic model for topic and role discovery in social networks: Experiments with Enron and academic email. Technical report, University of Massachusetts, Amherst.
21. Montoya R, Netzer O, Jedidi K (2010) Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Sci. 29(5):909–924.
22. Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.
23. Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.
24. Ramage D, Dumais ST, Liebling DJ (2010) Characterizing microblogs with topic models. Proc. Fourth Internat. AAAI Conf. Weblogs Social Media.
25. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The authortopic model for authors and documents. Proc. 20th Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 487–494.
26. Rossi PE, Allenby GM, McCulloch RE (2005) Bayesian Statistics and Marketing (John Wiley & Sons, West Sussex, UK).
27. Rossi PE, Gilula Z, Allenby GM (2001) Overcoming scale usage heterogeneity. J. Amer. Statist. Assoc. 96(453):20–31.
28. Tirunillai S, Tellis GJ (2014) Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. J. Marketing Res. 51(4):463–479.
29. Titov I, McDonald R (2008) A joint model of text and aspect ratings for sentiment summarization. Proc. ACL, Vol. 8, 308–316.
30. Wallach HM (2006) Topic modeling: Beyond bag-of-words. Proc. 23rd Internat. Conf. Machine Learn. (ACM, New York), 977–984.
31. Yang S, Allenby GM (2000) A model for observation, structural, and household heterogeneity in panel data. Marketing Lett. 11(2):137–149.
32. Yang S, Allenby GM, Fennel G (2002) Modeling variation in brand preference: The roles of objective environment and motivating conditions. Marketing Sci. 21(1):14–31.
33. Ying Y, Feinberg F, Wedel M (2006) Leveraging missing ratings to improve online recommendation systems. J. Marketing Res. 43(3):355–365.
34. Zhao Y, Yang S, Narayan V (2013) Modeling consumer learning from online product reviews. Marketing Sci. 32(1):153–169.