Natural Language Processing in Marketing
Abstract
The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Sentiment analysis represents one of the most prevalent use cases of automated text analysis in marketing.
“Sentiment analysis represents one of the most prevalent use cases of automated text analysis in marketing, offering deep insights into consumer emotions, opinions, and perceptions [26,30].”
From Sentiment Analysis in the Age of Generative AI (2024) · cites this paper as [26]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178 · see citing works
Incorporating non-local information into information extraction systems by Gibbs sampling
Finkel, J. R., Grenager, T., & Manning, C. D. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219885 · Cited by 3,028 · see citing works
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · see citing works
Concreteness ratings for 40 thousand generally known English word lemmas
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0403-5 · Cited by 1,821 · see citing works
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712 · see citing works
The Development and Psychometric Properties of LIWC2015
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015) · University of Texas at Austin · doi:10.15781/t25p41 · Cited by 1,674 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
Grootendorst, M. (2022) · arXiv · doi:10.48550/arxiv.2203.05794 · Cited by 1,423 · see citing works
Explainable AI: From black box to glass box
Rai, A. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00710-5 · Cited by 1,317 · see citing works
LAION-5B: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.08402 · Cited by 1,022 · see citing works
Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies
Hochreiter, S., Bengio, Y., Informatik, F. F., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001) · Cited by 863 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · see citing works
Topic Modeling in Embedding Spaces
Dieng, A. B., Ruiz, F. J. R., & Blei, D. M. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00325 · Cited by 825 · see citing works
Tirunillai, S., & Tellis, G. J. (2014) · Journal of Marketing Research · doi:10.1509/jmr.12.0106 · Cited by 780 · see citing works
Uniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742 · see citing works
Self-citationMine Your Own Business: Market-Structure Surveillance Through Text Mining
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012) · Marketing Science · doi:10.1287/mksc.1120.0713 · Cited by 741 · see citing works
Self-citationHigh-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works
Automated Text Analysis for Consumer Research
Humphreys, A., & Wang, R. J.-H. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucx104 · Cited by 636 · see citing works
Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance
Tirunillai, S., & Tellis, G. J. (2011) · Marketing Science · doi:10.1287/mksc.1110.0682 · Cited by 622 · see citing works
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
Yin, W., Hay, J., & Roth, D. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1404 · Cited by 537 · see citing works
Identifying Customer Needs from User-Generated Content
Timoshenko, A., & Hauser, J. R. (2019) · Marketing Science · doi:10.1287/mksc.2018.1123 · Cited by 513 · see citing works
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2021) · arXiv · doi:10.48550/arxiv.2101.00027 · Cited by 492 · see citing works
Automated Marketing Research Using Online Customer Reviews
Lee, T. Y., & Bradlow, E. T. (2011) · Journal of Marketing Research · doi:10.1509/jmkr.48.5.881 · Cited by 463 · see citing works
The Woman Worked as a Babysitter: On Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · see citing works
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Rothe, S., Narayan, S., & Severyn, A. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00313 · Cited by 400 · see citing works
Comparing automated text classification methods
Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.09.009 · Cited by 399 · see citing works
Self-citationUnderspecification Presents Challenges for Credibility in Modern Machine Learning
D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.03395 · Cited by 398 · see citing works
Accurate estimation of influenza epidemics using Google search data via ARGO
Yang, S., Santillana, M., & Kou, S. (2015) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1515373112 · Cited by 372 · see citing works
Sentence-Based Text Analysis for Customer Reviews
Büschken, J., & Allenby, G. M. (2016) · Marketing Science · doi:10.1287/mksc.2016.0993 · Cited by 361 · see citing works
More than a Feeling: Accuracy and Application of Sentiment Analysis
Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2023) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2022.05.005 · Cited by 356 · see citing works
Self-citationThe unreasonable effectiveness of deep learning in artificial intelligence
Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907373117 · Cited by 352 · see citing works
Emergent linguistic structure in artificial neural networks trained by self-supervision
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907367117 · Cited by 346 · see citing works
Efficient Few-Shot Learning Without Prompts
Tunstall, L., Reimers, N., Jo, U. E. S., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2209.11055 · Cited by 333 · see citing works
Ordenes, F. J. V., Grewal, D., Ludwig, S., de Ruyter, K., Mahr, D., & Wetzels, M. G. M. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucy032 · Cited by 331 · see citing works
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
Hu, J., Ruder, S., Siddhant, A., Neubig, G., Fırat, O., & Johnson, M. (2020) · International Conference on Machine Learning · Cited by 325 · see citing works
Listening in on Social Media: A Joint Model of Sentiment and Venue Format Choice
Schweidel, D. A., & Moe, W. W. (2014) · Journal of Marketing Research · doi:10.1509/jmr.12.0424 · Cited by 323 · see citing works
Unstructured data in marketing
Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300 · see citing works
Halo (Spillover) Effects in Social Media: Do Product Recalls of One Brand Hurt or Help Rival Brands?
Borah, A., & Tellis, G. J. (2015) · Journal of Marketing Research · doi:10.1509/jmr.13.0009 · Cited by 294 · see citing works
Large pre-trained language models contain human-like biases of what is right and wrong to do
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00458-8 · Cited by 291 · see citing works
Is Neutral Really Neutral? The Effects of Neutral User-Generated Content on Product Sales
Tang, T., Fang, E., & Wang, F. (2014) · Journal of Marketing · doi:10.1509/jm.13.0301 · Cited by 284 · see citing works
Hartmann, J., Schwenzow, J., & Witte, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4316084 · Cited by 273 · see citing works
Self-citationFrom Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts
Eliashberg, J., Hui, S. K., & Zhang, Z. J. (2007) · Management Science · doi:10.1287/mnsc.1060.0668 · Cited by 246 · see citing works
When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications
Netzer, O., Lemaire, A., & Herzenstein, M. (2019) · Journal of Marketing Research · doi:10.1177/0022243719852959 · Cited by 238 · see citing works
Self-citationJaidka, K., Giorgi, S., Schwartz, H. A., Kern, M. L., Ungar, L., & Eichstaedt, J. C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906364117 · Cited by 223 · see citing works
How Concrete Language Shapes Customer Satisfaction
Packard, G. M., & Berger, J. A. (2020) · Journal of Consumer Research · doi:10.1093/jcr/ucaa038 · Cited by 199 · see citing works
Large language models associate Muslims with violence
Abid, A., Farooqi, M., & Zou, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00359-2 · Cited by 193 · see citing works
Conducting Research in Marketing with Quasi-Experiments
Avi Goldfarb, B., Tucker, C. E., & Wang, Y. (2022) · Journal of Marketing · doi:10.1177/00222429221082977 · Cited by 191 · see citing works
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Sun, Y., Wang, S., Feng, S., Ding, S., Pang, C., Shang, J., Liu, J., Chen, X., Zhao, Y., Lu, Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.02137 · Cited by 186 · see citing works
Hartmann, J., Heitmann, M., Schamp, C., & Netzer, O. (2021) · Journal of Marketing Research · doi:10.1177/00222437211037258 · Cited by 184 · see citing works
Self-citationLiu, X., Lee, D., & Srinivasan, K. (2019) · Journal of Marketing Research · doi:10.1177/0022243719866690 · Cited by 179 · see citing works
Diachronic Word Embeddings Reveal Statistical Laws of Semantic ChangePreprint
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09096 · Cited by 177 · see citing works
Selectively Emotional: How Smartphone Use Changes User-Generated Content
Melumad, S., Inman, J. J., & Pham, M. T. (2019) · Journal of Marketing Research · doi:10.1177/0022243718815429 · Cited by 164 · see citing works
Fields of Gold: Scraping Web Data for Marketing Insights
Boegershausen, J., Datta, H., Borah, A., & Stephen, A. T. (2022) · Journal of Marketing · doi:10.1177/00222429221100750 · Cited by 155 · see citing works
Video Content Marketing: The Making of Clips
Liu, X., Shi, S. W., Teixeira, T. S., & Wedel, M. (2018) · Journal of Marketing · doi:10.1509/jm.16.0048 · Cited by 143 · see citing works
The Enhancing Versus Backfiring Effects of Positive Emotion in Consumer Reviews
Rocklage, M. D., & Fázio, R. H. (2020) · Journal of Marketing Research · doi:10.1177/0022243719892594 · Cited by 142 · see citing works
Extracting brand information from social networks: Integrating image, text, and social tagging data
Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.08.002 · Cited by 140 · see citing works
Toubia, O., Iyengar, G., Bunnell, R., & Lemaire, A. (2018) · Journal of Marketing Research · doi:10.1177/0022243718820559 · Cited by 139 · see citing works
Bovet, A., Morone, F., & Makse, H. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26951-y · Cited by 135 · see citing works
Idea Generation, Creativity, and Prototypicality
Toubia, O., & Netzer, O. (2016) · Marketing Science · doi:10.1287/mksc.2016.0994 · Cited by 128 · see citing works
Self-citationFrontiers: Supporting Content Marketing with Natural Language Generation
Reisenbichler, M., Reutterer, T., Schweidel, D. A., & Dan, D. (2022) · Marketing Science · doi:10.1287/mksc.2022.1354 · Cited by 126 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level ConstraintsPreprint
Zhao, J., Wang, T., Yatskar, M., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09457 · Cited by 122 · see citing works
Automatic Text Summarization by paragraph Extraction
Mitrat, M., Singhal, A., & Buckleytt, C. (1997) · Cited by 121 · see citing works
Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?
Kübler, R. V., Colicev, A., & Pauwels, K. H. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2019.08.001 · Cited by 121 · see citing works
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Keith, K. A., Jensen, D. D., & O’Connor, B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.474 · Cited by 110 · see citing works
Marketing Insights from Multimedia Data: Text, Image, Audio, and Video
Grewal, R., Gupta, S., & Hamilton, R. J. (2021) · Journal of Marketing Research · doi:10.1177/00222437211054601 · Cited by 108 · see citing works
The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language
Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0975-6 · Cited by 103 · see citing works
FLERT: Document-Level Features for Named Entity RecognitionPreprint
Schweter, S., & Akbik, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06993 · Cited by 103 · see citing works
Puranam, D., Narayan, V., & Kadiyali, V. (2017) · Marketing Science · doi:10.1287/mksc.2017.1048 · Cited by 102 · see citing works
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries
Liu, J., & Toubia, O. (2018) · Marketing Science · doi:10.1287/mksc.2018.1112 · Cited by 102 · see citing works
Historical representations of social groups across 200 years of word embeddings from Google Books
Charlesworth, T., Caliskan, A., & Banaji, M. (2022) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2121798119 · Cited by 98 · see citing works
Harvesting Brand Information from Social Tags
Nam, H., Joshi, Y. V., & Kannan, P. (2017) · Journal of Marketing · doi:10.1509/jm.16.0044 · Cited by 96 · see citing works
Dew, R., Ansari, A., & Toubia, O. (2021) · Marketing Science · doi:10.1287/mksc.2021.1326 · Cited by 93 · see citing works
Chakraborty, I., Kim, M., & Sudhir, K. (2022) · Journal of Marketing Research · doi:10.1177/00222437211052500 · Cited by 84 · see citing works
Adapting Text Embeddings for Causal InferencePreprint
Veitch, V., Sridhar, D., & Blei, D. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12741 · Cited by 81 · see citing works
How quantifying the shape of stories predicts their success
Toubia, O., Berger, J. A., & Eliashberg, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011695118 · Cited by 78 · see citing works
Designing Effective Sparse Expert Models
Zoph, B. (2022) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw55747.2022.00171 · Cited by 69 · see citing works
Incentives Increase Relative Positivity of Review Content and Enjoyment of Review Writing
Woolley, K., & Sharif, M. A. (2021) · Journal of Marketing Research · doi:10.1177/00222437211010439 · Cited by 61 · see citing works
Mining Consumer Minds: Downstream Consequences of Host Motivations for Home-Sharing Platforms
Chung, J., Johar, G. V., Li, Y., Netzer, O., & Pearson, M. (2021) · Journal of Consumer Research · doi:10.1093/jcr/ucab034 · Cited by 57 · see citing works
Self-citationHow to Make Causal Inferences Using TextsPreprint
Egami, N., Fong, C., Grimmer, J., Roberts, M. E., & Stewart, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02163 · Cited by 53 · see citing works
Thinking of You: How Second-Person Pronouns Shape Cultural Success
Packard, G. M., & Berger, J. A. (2020) · Psychological Science · doi:10.1177/0956797620902380 · Cited by 51 · see citing works
Mass-scale emotionality reveals human behaviour and marketplace success
Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01098-5 · Cited by 50 · see citing works
Dynamics of Musical Success: A Machine Learning Approach for Multimedia Data Fusion
Boughanmi, K., & Ansari, A. (2021) · Journal of Marketing Research · doi:10.1177/00222437211016495 · Cited by 50 · see citing works
Puranam, D., Kadiyali, V., & Narayan, V. (2021) · Marketing Science · doi:10.1287/mksc.2021.1294 · Cited by 45 · see citing works
Carlson, K., Kopalle, P. K., Riddell, A. B., Rockmore, D. N., & Vana, P. (2022) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2022.02.004 · Cited by 45 · see citing works
Li, X., Liao, C., & Xie, Y. (2021) · Marketing Science · doi:10.1287/mksc.2020.1275 · Cited by 38 · see citing works
R2M Index 1.0: Assessing the Practical Relevance of Academic Marketing Articles
Jedidi, K., Schmitt, B. H., Sliman, M. B., & Li, Y. (2021) · Journal of Marketing · doi:10.1177/00222429211028145 · Cited by 34 · see citing works
Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions
Feldman, R., Netzer, O., Peretz, A., & Rosenfeld, B. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788608 · Cited by 33 · see citing works
Self-citationContent-Based Model of Web Search Behavior: An Application to TV Show Search
Liu, J., Toubia, O., & Hill, S. (2019) · Management Science · doi:10.1287/mnsc.2020.3827 · Cited by 32 · see citing works
Wordify: A Tool for Discovering and Differentiating Consumer Vocabularies
Hovy, D., Melumad, S., & Inman, J. J. (2021) · Journal of Consumer Research · doi:10.1093/jcr/ucab018 · Cited by 26 · see citing works
Demand for Online News under Government Control: Evidence from Russia
Simonov, A., & Rao, J. M. (2021) · Journal of Political Economy · doi:10.1086/717351 · Cited by 22 · see citing works
Shahzad, M., Amin, A., Esteves, D., et al. (2021) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v34i1.128538 · Cited by 21 · see citing works
Commentary: Mind Your Text in Marketing Practice
Chapman, C. (2019) · Journal of Marketing · doi:10.1177/0022242919886882 · Cited by 17 · see citing works
Humphreys, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-05542-8_26-1 · Cited by 8 · see citing works
sDTM: A Supervised Bayesian Deep Topic Model for Text AnalyticsPreprint
Yang, Y., & Zhang, K. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3612168 · Cited by 6 · see citing works
An Analysis of Negation in Natural Language Understanding CorporaPreprint
Hossain, M. M., Chinnappa, D., & Blanco, E. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.08929 · Cited by 3 · see citing works
OPT: Open Pre-trained Transformer Language Models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Workshop, B., :, Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., et al. (2022) · arXiv · doi:10.48550/arxiv.2211.05100
Flagged first, then by citations
Cited by
Sorted by citations
Sentiment Analysis in the Age of Generative AI
Krugmann, J. O., & Hartmann, J. (2024) · Customer Needs and Solutions · doi:10.1007/s40547-024-00143-4 · Cited by 189 · see citing works
Self-citation
- Authors
- Hartmann, J., & Netzer, O.
- Affiliations
- Technical University of Munich (DE)
- bColumbia Business School, USA
- Venue
- Artificial Intelligence in Marketing
- Publisher
- Emerald Publishing Limited
- Published
- 2023-03-13
- Pages
- 191-215
- OpenAlex
- W4322507136
- Cited by
- 38
- References
- 117
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 12 · 2023: 5 · 2022: 1
- FWCI
- 20.59
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Emerald Group Publishing Limited eBooks (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- text data, topic modeling, sentiment analysis, vocabulary, pre-trained language models, transfer learning, text generation, multimodal representation learning, text summarization, generative question answering, text classification, online customer reviews, voice assistants, customer satisfaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
