Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach

Published Online:https://doi.org/10.1287/mnsc.2022.4443

References

  • Ansari A, Li Y, Zhang JZ (2018) Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Sci. 37(6):987–1008.LinkGoogle Scholar
  • Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann. Statist. (Institute of Mathematical Statistics), 47(2):1148–1178.Google Scholar
  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. B 57(1):289–300.Google Scholar
  • Berger J, Humphreys A, Ludwig S, Moe WW, Netzer O, Schweidel DA (2020) Uniting the tribes: Using text for marketing insight. J. Marketing 84(1):1–25.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3:993–1022.Google Scholar
  • Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.CrossrefGoogle Scholar
  • Carmeli N, Wang X, Suhara Y, Angelidis S, Li Y, Li J, Tan WC (2021) Constructing explainable opinion graphs from reviews. Leskovec J, Grobelnik M, Najork M, Tang J, Zia L, eds. Proc. Web Conference (ACM, New York), 3419–3431.Google Scholar
  • Cohen J (1960) A coefficient of agreement for nominal scales. Edu. Psych. Measures 20(1):37–46.CrossrefGoogle Scholar
  • Crump RK, Hotz VJ, Imbens GW, Mitnik OA (2009) Dealing with limited overlap in estimation of average treatment effects. Biometrika 96(1):187–199.CrossrefGoogle Scholar
  • Dens N, De Pelsmacker P, Goos P, Aleksandrovs L, Martens D (2018) How consumers’ media usage creates synergy in advertising campaigns. Internat. J. Marketing Res. 60(3):268–287.Google Scholar
  • Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Burstein J, Doran C, Solorio T, eds. Proc. Conf. of the North Amer. Chapter of the Assoc. for Comput. Linguistics: Human Language Technologies, vol. 1 (ACL, Stroudsburg, PA), 4171–4186.Google Scholar
  • Fan J, Fan Y (2008) High dimensional classification using features annealed independence rules. Ann. Statist. 36(6):2605.CrossrefGoogle Scholar
  • Fan Y, Kong Y, Li D, Zheng Z (2015) Innovated interaction screening for high-dimensional nonlinear classification. Ann. Statist. (Institute of Mathematical Statistics), 43(3):1243–1272.Google Scholar
  • Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psych. Bull. 76(5):378.CrossrefGoogle Scholar
  • Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics. Internat. J. Inform. Management 35(2):137–144.CrossrefGoogle Scholar
  • He R, Lee WS, Ng HT, Dahlmeier D (2017) An unsupervised neural attention model for aspect extraction. Barzilay R, Kan MY, eds. Proc. 55th Annual Meeting of the Assoc. for Comput. Linguistics, vol. 1 (ACL, Stroudsburg, PA), 388–397.Google Scholar
  • Ji Y, Smith NA (2017) Neural discourse structure for text categorization. Barzilay R, Kan MY, eds. Proc. 55th Annual Meeting of the Assoc. for Comput. Linguistics, vol. 1 (ACL, Stroudsburg, PA), 996–1005.Google Scholar
  • Jindal RP, Sarangee KR, Echambadi R, Lee S (2016) Designed to succeed: Dimensions of product design and their impact on market share. J. Marketing 80(4):72–89.CrossrefGoogle Scholar
  • Kannan P, Li H, et al. (2017) Digital marketing: A framework, review and research agenda. Internat. J. Res. Marketing (Elsevier), 34(1):22–45.CrossrefGoogle Scholar
  • Kiss T, Strunk J (2006) Unsupervised multilingual sentence boundary detection. Comput. Linguistics 32(4):485–525.CrossrefGoogle Scholar
  • Kobayashi N, Hirao T, Kamigaito H, Okumura M, Nagata M (2020) Top-down RST parsing utilizing granularity levels in documents. Proc. 34th AAAI Conf. on Artificial Intelligence, (AAAI, Palo Alto, CA), 8099–8106.Google Scholar
  • Kolluru K, Adlakha V, Aggarwal S, Mausam, Chakrabarti S (2020) OpenIE6: Iterative grid labeling and coordination analysis for open information extraction. Webber B, Cohn T, He Y, Liu Y, eds. Proc. Conf. on Empirical Methods in Natural Language Processing (ACL, Stroudsburg, PA), 3748–3761.Google Scholar
  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174.CrossrefGoogle Scholar
  • Lee JD, Sun DL, Sun Y, Taylor JE (2016) Exact post-selection inference, with application to the lasso. Ann. Statist. (Institute of Mathematical Statistics), 44(3):907–927.CrossrefGoogle Scholar
  • Li Y, Feng A, Li J, Mumick S, Halevy A, Li V, Tan WC (2019) Subjective databases. Proceedings VLDB Endowment, 12(11):1330–1343.CrossrefGoogle Scholar
  • Lin Z, Ng HT, Kan MY (2014) A PDTB-styled end-to-end discourse parser. Natural Language Engrg. 20(2):151–184.CrossrefGoogle Scholar
  • Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. Proc. 7th Internat. Conf. on Learning Representations (New Orleans, LA), 1–8.Google Scholar
  • Miller GA (1995) Wordnet: A lexical database for English. Comm. ACM 38(11):39–41.CrossrefGoogle Scholar
  • Mittal V, Ross WT, Baldasare PM (1998) The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. J. Marketing 62:33–47.CrossrefGoogle Scholar
  • Naik PA, Raman K (2003) Understanding the impact of synergy in multimedia communications. J. Marketing Res. 40(4):375–388.CrossrefGoogle Scholar
  • 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.LinkGoogle Scholar
  • Pelleg D, Moore A (2000) X-means: Extending k-means with efficient estimation of the number of clusters. Langley P, ed. Proc. 17th Internat Conf. on Machine Learn. (Morgan Kaufmann Publishers Inc., San Francisco), 727–734.Google Scholar
  • Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. Natural Language Processing and Text Mining (Springer, Berlin), 9–28.CrossrefGoogle Scholar
  • Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Inui K, Jiang J, Ng V, Wan X, eds. Proc. Conf. on Empirical Methods in Natural Language Processing and the 9th Internat. Joint Conf. on Natural Language Processing (ACL, Stroudsburg, PA), 3973–3983.Google Scholar
  • Rendle S (2010) Factorization machines. Proc. IEEE Internat. Conf. on Data Mining (IEEE, Piscataway, NJ), 995–1000.Google Scholar
  • Robins JM, Rotnitzky A, Zhao LP (1994) Estimation of regression coefficients when some regressors are not always observed. J. Amer. Statist. Assoc. 89(427):846–866.CrossrefGoogle Scholar
  • Robinson PM (1988) Root-n-consistent semiparametric regression. Econometrica: J. Econometric Soc. 56(4):931–954.CrossrefGoogle Scholar
  • Rocktäschel T, Grefenstette E, Hermann KM, Kočiskỳ T, Blunsom P (2015) Reasoning about entailment with neural attention. Preprint, submitted September 22, https://arxiv.org/abs/1509.06664.Google Scholar
  • Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric Regression (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Shine BC, Park J, Wyer RS Jr (2007) Brand synergy effects in multiple brand extensions. J. Marketing Res. 44(4):663–670.CrossrefGoogle Scholar
  • Taylor J, Tibshirani RJ (2015) Statistical learning and selective inference. Proc. National Acad. Sci. USA 112(25):7629–7634.CrossrefGoogle Scholar
  • Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.LinkGoogle Scholar
  • Van Der Laan MJ, Rubin D (2006) Targeted maximum likelihood learning. Internat. J. Biostatist. 2(1).CrossrefGoogle Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Luxburg UV, Guyon I, Bengio S, Wallach H, Fergus R, eds. Proc. 31st Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 6000–6010.Google Scholar
  • Wang J, Lu W (2020) Two are better than one: Joint entity and relation extraction with table-sequence encoders. Webber B, Cohn T, He Y, Liu Y, eds. Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (ACL, Stroudsburg, PA), 1706–1721.Google Scholar
  • Westreich D, Lessler J, Funk MJ (2010) Propensity score estimation: Neural networks, support vector machines, decision trees (cart), and meta-classifiers as alternatives to logistic regression. J. Clinical Epidemiology 63(8):826–833.CrossrefGoogle Scholar
  • Wu Z, Ying C, Zhao F, Fan Z, Dai X, Xia R (2020) Grid tagging scheme for aspect-oriented fine-grained opinion extraction. Cohn T, He Y, Liu Y, eds. Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (ACL, Stroudsburg, PA), 2576–2585.Google Scholar
  • Zhao Q, Small DS, Bhattacharya BB (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap. J. Roy. Statist. Soc. Ser. B Statist. Methodology 81(4):735–761.CrossrefGoogle Scholar
  • Zhao Q, Small DS, Ertefaie A (2022) Selective inference for effect modification via the lasso. J. Roy. Statist. Soc. Ser. B Statist. Methodology 84(2):382–413.CrossrefGoogle Scholar
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