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Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation
Abstract
Online chatter, or user-generated content, constitutes an excellent emerging source for marketers to mine meaning at a high temporal frequency. This article posits that this meaning consists of extracting the key latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions. The authors propose a unified framework for this purpose using unsupervised latent Dirichlet allocation. The sample of user-generated content consists of rich data on product reviews across 15 firms in five markets over four years. The results suggest that a few dimensions with good face validity and external validity are enough to capture quality. Dynamic analysis enables marketers to track dimensions’ importance over time and allows for dynamic mapping of competitive brand positions on those dimensions over time. For vertically differentiated markets (e.g., mobile phones, computers), objective dimensions dominate and are similar across markets, heterogeneity is low across dimensions, and stability is high over time. For horizontally differentiated markets (e.g., shoes, toys), subjective dimensions dominate but vary across markets, heterogeneity is high across dimensions, and stability is low over time.
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
The Role of Market Forces in Assuring Contractual Performance
Klein, B., & Leffler, K. B. (1981) · Journal of Political Economy · doi:10.1086/260996 · Cited by 3,653
The long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869
The Financial Information Content of Perceived Quality
Aaker, D. A., & Jacobson, R. (1994) · Journal of Marketing Research · doi:10.2307/3152193 · Cited by 789
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Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127
The role of data privacy in marketing
Martin, K. D., & Murphy, P. E. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0495-4 · Cited by 902
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Timoshenko, A., & Hauser, J. R. (2019) · Marketing Science · doi:10.1287/mksc.2018.1123 · Cited by 513
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Taecharungroj, V. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010035 · Cited by 489
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Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300
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McColl-Kennedy, J. R., Zaki, M., Lemon, K. N., Urmetzer, F., & Neely, A. (2019) · Journal of Service Research · doi:10.1177/1094670518812182 · Cited by 283
van Giffen, B., Herhausen, D., & Fahse, T. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.076 · Cited by 230
Measuring and Managing Consumer Sentiment in an Online Community Environment
Homburg, C., Ehm, L., & Artz, M. (2015) · Journal of Marketing Research · doi:10.1509/jmr.11.0448 · Cited by 196
Hartmann, J., Heitmann, M., Schamp, C., & Netzer, O. (2021) · Journal of Marketing Research · doi:10.1177/00222437211037258 · Cited by 184
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Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023) · Journal of Purchasing and Supply Management · doi:10.1016/j.pursup.2023.100823 · Cited by 144
Liu, X. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.02.021 · Cited by 140
Chakraborty, I., Kim, M., & Sudhir, K. (2022) · Journal of Marketing Research · doi:10.1177/00222437211052500 · Cited by 84
Gender bias in sentiment analysis
Thelwall, M. (2018) · Online Information Review · doi:10.1108/oir-05-2017-0139 · Cited by 78
“It is widely used within marketing and customer relations management through the online monitoring of customer opinions towards products and services expressed in social media or review sites (Pekar & Ou, 2008; Schweidel & Moe, 2014; Tirunillai & Tellis, 2014).”
BackgroundMoretto, A., Ronchi, S., & Patrucco, A. S. (2017) · International Journal of RF Technologies: Research and Applications · doi:10.3233/rft-171670 · Cited by 57
Combating Loneliness with Artificial Intelligence: An AI-Based Emotional Support Model
Sullivan, Y., Nyawa, S., & Fosso Wamba, S. (2023) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2023.541 · Cited by 44
Natural Language Processing in Marketing
Hartmann, J., & Netzer, O. (2023) · Artificial Intelligence in Marketing · doi:10.1108/s1548-643520230000020011 · Cited by 38
Do Marketers Matter for Entrepreneurs? Evidence from a Field Experiment in Uganda
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Metadata
- Authors
- Tirunillai, S., & Tellis, G. J.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2014-08
- Volume
- 51
- Issue
- 4
- Pages
- 463-479
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2132314509
- Cited by
- 774
- References
- 10
- FWCI
- 100.53
- Type
- journal article
- Language
- en
- Open access
- closed
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