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Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication

Abstract

Text-based electronic word-of-mouth (eWOM) communication has increasingly become an important channel for consumers to exchange information about products and services. How to effectively utilize the enormous amount of text information poses a great challenge to marketing researchers and practitioners. This study takes an initial step to investigate the validities and usefulness of text mining, a promising approach in generating valuable information from eWOM communication. Bilateral data were collected from both eWOM senders and readers via two web-based surveys. Results provide initial evidence for the validity and utility of text mining and demonstrate that the linguistic indicators generated by text analysis are predictive of eWOM communicators’ attitudes toward a product or service. Text analysis indicators (e.g., Negations and Money) can explain additional variance in eWOM communicators’ attitudes above and beyond the star ratings and may become a promising supplement to the widely used star ratings as indicators of eWOM valence.

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References

  • Aggarwal, P., Vaidyanathan, R., & Venkatesh, A. (2009). Using lexical semantic analysis to derive online brand positions: An application to retail marketing research. Journal of Retailing, 85(2), 145–158.

    Article  Google Scholar 

  • Ahluwalia, R. (2002). How prevalent is the negativity effect in consumer environments? Journal of Consumer Research, 29, 270–279.

    Article  Google Scholar 

  • Alpers, G., Winzelberg, A., Classen, C., Roberts, H., Dev, P., Koopman, C., & Taylor, C. (2005). Evaluation of computerized text analysis in an internet breast cancer support group. Computers in Human Behavior, 21(2), 361–376.

    Article  Google Scholar 

  • Bantum, E., & Owen, J. (2009). Evaluating the validity of computerized content analysis programs for identification of emotional expression in cancer narratives. Psychological Assessment, 21(1), 79–88.

    Article  Google Scholar 

  • Bohanek, J., Fivush, R., & Walker, E. (2005). Memories of positive and negative emotional events. Applied Cognitive Psychology, 19(1), 51–66.

    Article  Google Scholar 

  • Cronbach, L. J., & Meehl, P. E. (1955). Construct validity for psychological tests. Psychological Bulletin, 52, 281–302.

    Article  Google Scholar 

  • Feldman, J. M., & Lynch, J. G. (1988). Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology, 73(3), 421–435.

    Article  Google Scholar 

  • Gotlieb, J., & Sarel, D. (1991). Comparative advertising effectiveness: The role of involvement and source credibility. Journal of Advertising, 20(1), 38–45.

    Article  Google Scholar 

  • Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60–76.

    Article  Google Scholar 

  • Hancock, J., Landrigan, C., & Silver, C. (2007). Expressing emotion in text-based communication. In CHI 2007 proceedings of the SIGCHI conference on human factors in computing systems (pp. 929–932).

  • Henning-Thurau, T., Gwinner, K., Walsh, G., & Gremler, D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–51.

    Article  Google Scholar 

  • Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility–diagnosticity perspective. Journal of Consumer Research, 17(March), 454–462.

    Article  Google Scholar 

  • Holbrook, M., & Batra, R. (1987). Assessing the role of emotions as mediators of consumer responses to advertising. Journal of Consumer Research, 14(3), 404–420.

    Article  Google Scholar 

  • Jansen, J. (2010, September 29). Attention shopper: Online product research. Pew Internet and American Life Project, available at www.pewinternet.org/pubs/1747/e-shopping-researched-product-service.aspx (Accessed 1/25/2012).

  • Kahn, J., Tobin, R., Massey, A., & Anderson, J. (2007). Measuring emotional expression with the linguistic inquiry and word count. American Journal of Psychology, 120(2), 263–286.

    Google Scholar 

  • Kleij, F., & Musters, P. (2003). Text analysis of open-ended survey responses: A complementary method to preference mapping. Food Quality and Preference, 14(1), 43–52.

    Article  Google Scholar 

  • Kozinets, R. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72.

    Article  Google Scholar 

  • Lehto, X., Park, J., Park, O., & Lehto, M. (2007). Text analysis of consumer reviews: The case of virtual travel firms. In M.J. Smith, G. Salvendy (Eds.), Human Interface, Part I, HCII 2007, LNCS4557, 490–99.

  • Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  • Mehl, M. R. (2006). Quantitative text analysis. In M. Eid & E. Diener (Eds.), Handbook of multimethod measurement in psychology (pp. 141–156). Washington, DC: American Psychology Association.

    Chapter  Google Scholar 

  • Mehl, M. R. (2010). Automatic text analysis. In S. D. Gosling & J. A. Johnson (Eds.), Advanced methods for behavioral research on the Internet (pp. 109–127). Washington, DC: American Psychology Association.

    Google Scholar 

  • Owen, J., Giese-Davis, J., Cordova, M., Kronenwetter, C., Golant, M., & Spiegel, D. (2006). Self-report and linguistic indicators of emotional expression in narratives as predictors of adjustment to cancer. Journal of Behavioral Medicine, 29(4), 335–345.

    Article  Google Scholar 

  • Pennebaker, J., Mayne, T., & Francis, M. (1997). Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology, 72(4), 863–871.

    Article  Google Scholar 

  • Pennebaker, J., Mehl, M., & Niederhoffer, K. (2003). Psychological aspects of natural language: Our words, our selves. Annual Review of Psychology, 54(1), 547–577.

    Article  Google Scholar 

  • Ramanathan, V., & Meyyappan, T. (2013). Survey of text mining. In Proceedings of International Conference on Technology and Business Management (pp. 508–514).

    Google Scholar 

  • Sirdhar, S., & Srinivasan, R. S. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70–88.

    Article  Google Scholar 

  • Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24–54.

    Article  Google Scholar 

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Correspondence to Chuanyi Tang.

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Tang, C., Guo, L. Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication. Mark Lett 26, 67–80 (2015). https://doi.org/10.1007/s11002-013-9268-8

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  • DOI: https://doi.org/10.1007/s11002-013-9268-8

Keywords

  • Electronic word-of-mouth
  • Text mining
  • Linguistic indicator
  • Attitude