City Research Online

Abstract

Deciphering consumers' sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers' behavior and are generalizable to other social media contexts, such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.

Publication Type:	Article
Additional Information:	This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Consumer Research following peer review. The version of record Ordenes, F. V., Ludwig, S., de Ruyter, K., Grewal, D. & Wetzels, M. (2017). Unveiling What is Written in The Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media. Journal of Consumer Research, is available online at: http://dx.doi.org/10.1093/jcr/ucw070
Publisher Keywords:	Consumer Sentiment, Sentiment Analysis, Speech Act Theory, Text Mining, Customer Reviews, Sales Ranks, Social Media, Marketing Analytics
Subjects:	H Social Sciences > HD Industries. Land use. Labor
Departments:	Bayes Business School > Management
SWORD Depositor:	Symplectic Administrator

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