Harvesting Brand Information from Social Tags

Social tags are user-defined keywords associated with online content that reflect consumers’ perceptions of various objects, including products and brands. This research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. The authors first compare their proposed approach with conventional techniques such as brand concept maps and text mining. They highlight the added value of their approach that results from the unconstrained, open-ended, and synoptic nature of consumer-generated content contained within social tags. The authors then apply existing text-mining and data-reduction methods to analyze disaggregate-level social tagging data for marketing research and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.

[1]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[2]  Michel Wedel,et al.  The effects of alternative methods of collecting similarity data for Multidimensional Scaling , 1995 .

[3]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[4]  Daniel M. Ringel,et al.  Visualizing Asymmetric Competition Among More Than 1, 000 Products Using Big Search Data , 2016, Mark. Sci..

[5]  Meredith Ringel Morris,et al.  #TwitterSearch: a comparison of microblog search and web search , 2011, WSDM '11.

[6]  Saeideh Bakhshi,et al.  "I need to try this"?: a statistical overview of pinterest , 2013, CHI.

[7]  Deborah Roedder John,et al.  Brand Concept Maps: A Methodology for Identifying Brand Association Networks , 2006 .

[8]  Daniel Aloise,et al.  Extracting Summary Piles from Sorting Task Data , 2017 .

[9]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[10]  Barton A. Weitz,et al.  Substitution in Use and the Role of Usage Context in Product Category Structures , 1991 .

[11]  W. Kamakura,et al.  Quantitative Trendspotting , 2012 .

[12]  William Rand,et al.  Brand Buzz in the Echoverse , 2016 .

[13]  Simon J. Blanchard,et al.  Estimating Multiple Consumer Segment Ideal Points from Context-Dependent Survey Data , 2008 .

[14]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[15]  P. Wang,et al.  ATM network: goals and challenges , 1995, CACM.

[16]  Simon J. Blanchard,et al.  A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification , 2012, Psychometrika.

[17]  Jacob Goldenberg,et al.  Mine Your Own Business: Market-Structure Surveillance Through Text Mining , 2012, Mark. Sci..

[18]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[19]  Indrajit Sinha,et al.  A stochastic multidimensional unfolding approach for representing phased decision outcomes , 1996 .

[20]  G. Zaltman,et al.  Seeing the Voice of the Customer: Metaphor-based Advertising Research , 1995 .

[21]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Steven M. Shugan.  Estimating Brand Positioning Maps Using Supermarket Scanning Data , 1987 .

[23]  Christopher Joiner,et al.  Concept Mapping in Marketing: a Research Tool For Uncovering Consumers Knowledge Structure Associations , 1998 .

[24]  P. K. Kannan,et al.  Consumer substitution decisions: an integrative framework , 2014 .

[25]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[26]  Adrian E. Raftery,et al.  Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering , 2007, J. Classif..

[27]  G. Zaltman.  Rethinking Market Research: Putting People Back In , 1997 .

[28]  Mor Naaman,et al.  Why we tag: motivations for annotation in mobile and online media , 2007, CHI.

[29]  P. K. Kannan,et al.  The Informational Value of Social Tagging Networks , 2014 .

[30]  Sam K. Hui,et al.  Model-based analysis of concept maps , 2008 .

[31]  Vrinda Kadiyali,et al.  The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors , 2017, Mark. Sci..

[32]  C A Nelson,et al.  Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.

[33]  Barbara Bickart,et al.  Brand Equity Dilution: Retailer Display and Context Brand Effects , 1999 .

[34]  Markus Strohmaier,et al.  Why do Users Tag? Detecting Users' Motivation for Tagging in Social Tagging Systems , 2010, ICWSM.

[35]  Efthimis N. Efthimiadis,et al.  Conversational tagging in twitter , 2010, HT '10.

[36]  Ian T. Jolliffe,et al.  Estimating common trends in multivariate time series using dynamic factor analysis , 2003 .

[37]  J. Carroll,et al.  Interpoint Distance Comparisons in Correspondence Analysis , 1986 .

[38]  Aron Culotta,et al.  Mining Brand Perceptions from Twitter Social Networks , 2016, Mark. Sci..

[39]  Marketing Applications of Social Tagging Networks , 2012 .

[40]  Valentin Robu,et al.  Emergence of consensus and shared vocabularies in collaborative tagging systems , 2009, TWEB.

[41]  G. Tellis,et al.  Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation , 2014 .

[42]  Eric T. Bradlow,et al.  Automated Marketing Research Using Online Customer Reviews , 2011 .