Unveiling what is written in the stars: Analyzing explicit, implicit, and discourse patterns of sentiment in social media

Francisco Villarroel Ordenes, Stephan Ludwig, Ko De Ruyter, Dhruv Grewal, Martin Wetzels

Research output: Contribution to journalArticle

72 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)875-894
Number of pages20
JournalJournal of Consumer Research
Issue number6
Early online date23 Jan 2017
Publication statusPublished - 30 Apr 2017


  • Consumer sentiment
  • Online reviews
  • Sales ranks
  • Social media
  • Speech act theory
  • Text mining

ASJC Scopus subject areas

  • Business and International Management
  • Anthropology
  • Arts and Humanities (miscellaneous)
  • Economics and Econometrics
  • Marketing

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