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

Marketing Letters

Volumn 26, Issue 1, 2015, Pages 67-80

  Tang, Chuanyi ^a   Guo, Lin ^b  

^a OLD DOMINION UNIVERSITY   (United States)

^b UNIVERSITY OF NEW HAMPSHIRE   (United States)

Author keywords

Attitude; Electronic word of mouth; Linguistic indicator; Text mining

Indexed keywords

EID: 84887334076     PISSN: 09230645     EISSN: 1573059X     Source Type: Journal    
DOI: 10.1007/s11002-013-9268-8     Document Type: Article

References (27)

1. 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.
2. Ahluwalia, R. (2002). How prevalent is the negativity effect in consumer environments? Journal of Consumer Research, 29, 270–279.
3. 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.
4. 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.
5. Bohanek, J., Fivush, R., & Walker, E. (2005). Memories of positive and negative emotional events. Applied Cognitive Psychology, 19(1), 51–66.
6. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity for psychological tests. Psychological Bulletin, 52, 281–302.
7. 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.
8. Gotlieb, J., & Sarel, D. (1991). Comparative advertising effectiveness: The role of involvement and source credibility. Journal of Advertising, 20(1), 38–45.
9. 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.
10. 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).
11. 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.
12. 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.
13. 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.
14. 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).
15. 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.
16. 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.
17. Kozinets, R. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72.
18. 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.
19. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.
20. 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.
21. 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.
22. 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.
23. Pennebaker, J., Mayne, T., & Francis, M. (1997). Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology, 72(4), 863–871.
24. Pennebaker, J., Mehl, M., & Niederhoffer, K. (2003). Psychological aspects of natural language: Our words, our selves. Annual Review of Psychology, 54(1), 547–577.
25. Ramanathan, V., & Meyyappan, T. (2013). Survey of text mining. In Proceedings of International Conference on Technology and Business Management (pp. 508–514).
26. Sirdhar, S., & Srinivasan, R. S. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70–88.
27. 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.