Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches

Apply Topic modeling to group synonyms under a topic to avoid human intervention and improve automatic market structure generation.Develop the WVAP method to filter noises in Topic modeling results to elicit market structure.Besides perceptual maps of product positioning, the proposed framework can provide rankings of products. Studies have shown that perceptual maps derived from online consumer-generated data are effective for depicting market structure such as demonstrating positioning of competitive brands. However, most text mining algorithms would require manual reading to merge extracted product features with synonyms. In response, Topic modeling is introduced to group synonyms together under a topic automatically, leading to convenient and accurate evaluation of brands based on consumers' online reviews. To ensure the feasibility of employing Topic modeling in assessing competitive brands, we developed a unique and novel framework named WVAP (Weights from Valid Posterior Probability) based on Scree plot technique. WVAP can filter the noises in posterior distribution obtained from Topic modeling, and improve accuracy in brand evaluation. A case study exploring online reviews of mobile phones is conducted. We extract topics to reflect the features of the cell phones with a qualified validity. In addition to perceptual maps derived by multi-dimensional scaling (MDS) for product positioning, we also rank these products by TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) so as to visualize the market structure from different perspectives. Our case study of cell phones shows that the proposed framework is effective in mining online reviews and providing insights into the competitive landscape.

[1]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[2]  Terry Elrod,et al.  Internal analysis of market structure: Recent developments and future prospects , 1991 .

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

[4]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

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

[6]  M. Keane,et al.  Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets , 1996 .

[7]  John R. Hauser,et al.  Testing Competitive Market Structures , 1984 .

[8]  John R. Hauser,et al.  “Listening In” to Find and Explore New Combinations of Customer Needs , 2004 .

[9]  Jacob Goldenberg,et al.  Extracting Product Comparisons from Discussion Boards , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[10]  K. Shao.  The Effects of Controversial Reviews on Product Sales Performance: The Mediating Role of the Volume of Word of Mouth , 2012 .

[11]  Arvind K. Tripathi,et al.  From Fatwallet to eBay: An investigation of online deal-forums and sales promotions , 2006 .

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

[13]  Akihiro Inoue,et al.  Building Market Structures from Consumer Preferences , 1996 .

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

[15]  Yue Pan,et al.  Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews , 2011 .

[16]  M. Laroche,et al.  Internet versus Bricks-and-Mortar Retailers: An Investigation into Intangibility and Its Consequences , 2005 .

[17]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[18]  Pradeep K. Chintagunta,et al.  Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets , 2006 .

[19]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[20]  Puneet Manchanda,et al.  Marketing Activity, Blogging and Sales , 2012 .

[21]  Peeter W. J. Verlegh,et al.  The Firm's Management of Social Interactions , 2005 .

[22]  T. Newkirk.  Listening In , 1992 .

[23]  Geir Sogn‐Grundvåg,et al.  Consumer evaluation of unbranded and unlabelled food products: The case of bacalhau , 2009 .

[24]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[25]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[27]  P. Berthon,et al.  Understanding Consumer Conversations Around Ads in a Web 2.0 World , 2011 .

[28]  Christopher K. Hsee,et al.  Narrow Focusing: Why the Relative Position of a Good in its Category Matters More than it Should , 2005 .

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

[30]  Wagner A. Kamakura,et al.  Inferring Market Structure from Customer Response to Competing and Complementary Products , 2001 .

[31]  Tong Zhang,et al.  Text Mining: Predictive Methods for Analyzing Unstructured Information , 2004 .

[32]  W. DeSarbo,et al.  Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis , 1991 .