Visual Listening In: Extracting Brand Image Portrayed on Social Media

Published Online:https://doi.org/10.1287/mksc.2020.1226

References

  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.LinkGoogle Scholar
  • Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. Guyon I, Dror G, Lemaire V, Taylor G, Silver D, eds. Proc. ICML Workshop Unsupervised Transfer Learn. (Bellevue, WA), 17–36.Google Scholar
  • Bengio Y, Bergeron A, Boulanger-Lewandowski N, Breuel T, Chherawala Y, Cisse M, Erhan D, et al. (2011) Deep learners benefit more from out-of-distribution examples. Gordon G, Dunson D, Miroslav D, eds. Proc. 14th Internat. Conf. Artificial Intelligence Statist., (Fort Lauderdale, FL), 164–172.Google Scholar
  • Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7):1145–1159.Google Scholar
  • Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J. Marketing Res. 43(3):345–354.CrossrefGoogle Scholar
  • Culotta A, Cutler J (2016) Mining brand perceptions from Twitter social networks. Marketing Sci. 35(3):343–362.LinkGoogle Scholar
  • Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. 2009 Conf. Comput. Vision Pattern Recognition (IEEE, New York), 248–255.Google Scholar
  • Dhar S, Ordonez V, Berg TL (2011) High level describable attributes for predicting aesthetics and interestingness. 2011 Conf. Comput. Vision Pattern Recognition (IEEE, New York), 1657–1664.Google Scholar
  • Diehl K, Zauberman G, Barasch A (2016) How taking photos increases enjoyment of experiences. J. Personality Soc. Psych. 111(2):119–140.CrossrefGoogle Scholar
  • Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) DeCAF: A deep convolutional activation feature for generic visual recognition. Proc. Machine Learn. Res. 32:647–655.Google Scholar
  • Gabel S, Guhl D, Klapper D (2019) P2v-map: Mapping market structures for large retail assortments. J. Marketing Res. 56(4):4557–4580.CrossrefGoogle Scholar
  • Gardner BB, Levy SJ (1955) The product and the brand. Harvard Bus. Rev. 33(2):33–39.Google Scholar
  • Giannakopoulos T, Papakostas M, Perantonis S, Karkaletsis V (2015) Visual sentiment analysis for brand monitoring enhancement. 2015 9th Internat. Sympos. Image Signal Processing Anal. (ISPA) (IEEE, New York), 1–6.Google Scholar
  • Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. 2014 Conf. Comput. Vision and Pattern Recognition (IEEE, New York), 580–587.Google Scholar
  • Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
  • Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. Proc. 22nd ACM Internat. Conf. Multimedia (ACM, New York), 675–678.Google Scholar
  • Jiang YG, Wang Y, Feng R, Xue X, Zheng Y, Yang H (2013) Understanding and predicting interestingness of videos. 27th AAAI Conf. Artificial Intelligence (ACM, New York), 1113–1119.Google Scholar
  • Karayev S, Trentacoste M, Han H, Agarwala A, Darrell T, Hertzmann A, Winnemoeller H (2014) Recognizing image style. Valstar M, French A, Pridmore T, eds. Proc. British Machine Vision Conf. (BMVA Press, Durham, UK).Google Scholar
  • Keller KL, Lehmann DR (2006) Brands and branding: Research findings and future priorities. Marketing Sci. 25(6):740–759.LinkGoogle Scholar
  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Processing Systems 25:1097–1105.Google Scholar
  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.CrossrefGoogle Scholar
  • Liu X, Lee D, Srinivasan K (2019) Large scale cross category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.CrossrefGoogle Scholar
  • Liu Y (2006) Word of mouth for movies: Its dynamics and impact on box office revenue. J. Marketing 70(3):74–89.CrossrefGoogle Scholar
  • Mahajan D, Girshick R, Ramanathan V, He K, Paluri M, Li Y, Bharambe A, van der Maaten L (2018) Exploring the limits of weakly supervised pretraining. Proc. Eur. Conf. Comput. Vision (ECCV) (Springer Science and Business Media, Berlin), 181–196.Google Scholar
  • McAuley J, Leskovec J (2012) Image labeling on a network: Using social-network metadata for image classification. Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C, eds. Eur. Conf. Comput. Vision (Springer, Berlin), 828–841.Google Scholar
  • Mizik N, Jacobson R (2008) The financial value impact of perceptual brand attributes. J. Marketing Res. 45(1):15–32.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
  • Park CW, Jaworski BJ, Maclnnis DJ (1986) Strategic brand concept-image management. J. Marketing 50:135–145.CrossrefGoogle Scholar
  • Pavlov E, Mizik N (2019) Increasing consumer engagement with firm-generated social media content: The role of images and words. Working paper, University of Washington, Seattle.Google Scholar
  • Raghubir P, Greenleaf EA (2006) Ratios in proportion: What should the shape of the package be? J. Marketing 70(2):95–107.CrossrefGoogle Scholar
  • Stadlen A (2015) Find every photo with Flickr’s new unified search experience. Accessed May 7, 2015, https://blog.flickr.net/en/2015/05/07/flickr-unified-search/.Google Scholar
  • Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.LinkGoogle Scholar
  • 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.CrossrefGoogle Scholar
  • Wedel M, Pieters R, eds. (2007) Visual Marketing (Lawrence Erlbaum Associates, New York).CrossrefGoogle Scholar
  • Wedel M, Pieters R (2014) The buffer effect: The role of color when advertising exposures are brief and blurred. Marketing Sci. 34(1):134–143.LinkGoogle Scholar
  • Xiao L, Ding M (2014) Just the faces: Exploring the effects of facial features in print advertising. Marketing Sci. 33(3):338–352.LinkGoogle Scholar
  • Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? Adv. Neural Inform. Processing Systems 27:3320–3328.Google Scholar
  • Zhang H, Korayem M, Crandall DJ, LeBuhn G (2012) Mining photo-sharing websites to study ecological phenomena. Proc. 21st Internat. Conf. World Wide Web (ACM, New York), 749–758.Google Scholar
  • Zhang J, Wedel M, Pieters R (2009) Sales effects of attention to feature advertisements: A Bayesian mediation analysis. J. Marketing Res. 46(5):669–681.CrossrefGoogle Scholar
  • Zhang M, Luo L (2019) Can user-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Working paper, University of Southern California, Los Angeles.Google Scholar
  • Zhang S, Lee D, Singh PV, Srinivasan K (2018) How much is an image worth? Airbnb property demand estimation leveraging large scale image analytics. Working paper, David S. Tepper Business School, Carnegie Mellon University.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.