Attribute-based sentiment analysis in marketing: Application and strategic implications

Issue title: Special Issue – SAS Global Forum 2018

Guest editors: Jennifer Waller and Tyler Smith

Article type: Research Article

Authors: Ghosh, Amit K.^{a; *} | Kotekal, Praveen^b | Chakraborty, Goutam^b

Affiliations: [a] Cleveland State University, Cleveland, OH 44114, USA | [b] Oklahoma State University, Stillwater, OK, USA

Correspondence: [*] Corresponding author: Amit K. Ghosh, Monte Ahuja College of Business, Cleveland State University, 1860 E. 18th Street, BU 460, Cleveland, OH 44114, USA. Tel.: +1 216 687 4770; E-mail: [email protected].

Abstract: Increasingly, customers use social media and other Internet-based applications (e.g., review sites) to voice their opinions and to express their sentiments about brands. These reviews influence brand identity, either directly (by affecting consumer behavior) or indirectly (by generating positive or negative word-of-mouth through online social networks). We present an automated methodology that can be used to collect data from popular brand review sites and discussion boards. Customer feedback is then analyzed using best-practices of text mining and supervised sentiment analysis.  Strategic implications of customer sentiments are discussed as we explore the role of sentiment analysis on modification of branding strategy.

Keywords: Attribute-based sentiment analysis, text mining, brand attributes, predictive modeling, brand management, supervised sentiment analysis

DOI: 10.3233/MAS-180441

Journal: Model Assisted Statistics and Applications, vol. 13, no. 4, pp. 311-318, 2018