Emotions in Product Reviews--Empirics and Models

Online communities provide Internet users with means to overcome some information barriers and constraints, such as the difficulty to gather independent information about products and firms. Product review communities allow customers to share their opinions and emotions after the purchase of a product. We introduce a new dataset of product reviews from Amazon.com, with emotional information extracted by sentiment detection tools. Our statistical analysis of this data provides evidence for the existence of polemic reviews, as well as for the coexistence of positive and negative emotions inside reviews. We find a strong bias towards large values in the expression of positive emotions, while negative ones are more evenly distributed. We identified different time dynamics of the creation of reviews dependent on the presence of marketing and word of mouth effects. We define an agent-based model of the users of product review communities using a modeling framework for online emotions. This model can reproduce the scenarios of response to external influences, as well as some properties of the distributions of positive and negative emotions expressed in product reviews. This analysis and model can provide guidelines to manufacturers on how to increase customer satisfaction and how to measure the emotional impact of marketing campaigns through reviews data.

[1]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[2]  J. Russell.  A circumplex model of affect. , 1980 .

[3]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[4]  Jan Lorenz,et al.  Universality in movie rating distributions , 2008, 0806.2305.

[5]  F. Tuerlinckx,et al.  Journal of Personality and Social Psychology Feelings Change : Accounting for Individual Differences in the Temporal Dynamics of Affect , 2010 .

[6]  S. Rosen.  The Economics of Superstars , 1981 .

[7]  Chrysanthos Dellarocas,et al.  Exploring the value of online product reviews in forecasting sales: The case of motion pictures , 2007 .

[8]  Fang Wu,et al.  Opinion formation under costly expression , 2010, TIST.

[9]  M. Bradley,et al.  Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .

[10]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[11]  Dania Bilal,et al.  Information and Emotion: The Emergent Affective Paradigm in Information Behavior Research and Theory , 2007 .

[12]  ThelwallMike,et al.  Sentiment strength detection in short informal text , 2010 .

[13]  Niels Ole Pors.  Information and Emotion: The Emergent Affective Paradigm in Information Behavior Research and Theory , 2008, J. Documentation.

[14]  Frank Schweitzer,et al.  An agent-based model of collective emotions in online communities , 2010, ArXiv.

[15]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[16]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[17]  Lorin M. Hitt,et al.  Self Selection and Information Role of Online Product Reviews , 2007, Inf. Syst. Res..