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Sentiment Analysis and the Impact of Employee Satisfaction on Firm Earnings

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Advances in Information Retrieval (ECIR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8416))

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Abstract

Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder group, namely, the firm’s employees. First, we present a novel dataset that uses online employee reviews to capture employee satisfaction. We employ LDA to identify salient aspects in employees’ reviews, and manually infer one latent topic that appears to be associated with the firm’s outlook. Second, we create a composite document by aggregating employee reviews for each firm and measure employee sentiment as the polarity of the composite document using the General Inquirer dictionary to count positive and negative terms. Finally, we define employee satisfaction as a weighted combination of the firm outlook topic cluster and employee sentiment. The results of our joint aspect-polarity model suggest that it may be beneficial for investors to incorporate a measure of employee satisfaction into their method for forecasting firm earnings.

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References

  1. Edmans, A.: Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices. Journal of Financial Economics 101(3) (2011)

    Google Scholar 

  2. Jeaneau, H., Hudson, J., Zlotnicka, E.: ESG Keys: Human Capital – Looking for questions (2013)

    Google Scholar 

  3. Jeaneau, H., Hudson, J., Zlotnicka, E.: Corporate culture: Relevant to investors? UBS Investment Research (2013)

    Google Scholar 

  4. Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (2004)

    Google Scholar 

  5. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (2004)

    Google Scholar 

  6. Hussaini, M., Kocyigit, A., Tapucu, D., Yanikoglu, B., Saygin, Y.: An aspect-lexicon creation and evaluation tool for sentiment analysis researchers. In: ECMLPKDD (2012)

    Google Scholar 

  7. Hogg, R., Tanis, E.: Probability and Statistical Inference, 8th edn. (2012)

    Google Scholar 

  8. Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Academic Press (1979)

    Google Scholar 

  9. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP 2002 (2002)

    Google Scholar 

  10. Brown, L.D.: Earnings forecasting research: Its implications for capital markets research. International Journal of Forecasting 9, 295–320 (1993)

    Article  Google Scholar 

  11. Titov, I., McDonald, R.: A Joint Model of Text and Aspect Ratings for Sentiment Summarization. In: Proceedings of the 46th ACL, pp. 308–316 (2008)

    Google Scholar 

  12. Tetlock, P.C.: Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62, 1139–1168 (2007)

    Article  Google Scholar 

  13. Blei, D.M., Ng, A., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  14. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Science 101, 5228–5235 (2004)

    Article  Google Scholar 

  15. Kennedy, A., Inkpen, D.: Sentiment Classification of Movie Reviews using Contextual Valence Shifters. Computational Intelligence 22(2), 110–125 (2006)

    Article  MathSciNet  Google Scholar 

  16. Tversky, A., Kahneman, D.: Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology 5(2) (1973)

    Google Scholar 

  17. Brambor, T., Clark, W.R., Golder, M.: Understanding Interaction Models: Improving Empirical Analyses. Political Analysis 14, 63–82 (2006)

    Article  Google Scholar 

  18. Mei, X.S., Zhai, C.: Automatic labelling of multinomial topic models. In: SIGKDD (2007)

    Google Scholar 

  19. Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (2002)

    Google Scholar 

  20. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (2005)

    Google Scholar 

  21. Ku, L.W., Lo, Y.S., Chen, H.H.: Test collection selection and gold standard generation for a multiply-annotated opinion corpus. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

    Google Scholar 

  22. Bernard, V., Thomas, T.: Evidence that stock prices do not fully reflect the implications of current earnings for future earnings. Journal of Accounting and Economics 13, 305–340 (1990)

    Article  Google Scholar 

  23. White, H.: A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica 48, 817–838 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  24. Fama, E.F., French, K.R.: The cross-section of expected stock returns. Journal of Finance 47 (1992)

    Google Scholar 

  25. Carhart, M.M.: On persistence in mutual fund performance. Journal of Finance 52, 57–82 (1997)

    Article  Google Scholar 

  26. Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall, New York (1993)

    Book  MATH  Google Scholar 

  27. Stone, P., Dumphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press (1966)

    Google Scholar 

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Moniz, A., de Jong, F. (2014). Sentiment Analysis and the Impact of Employee Satisfaction on Firm Earnings. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_51

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  • DOI: https://doi.org/10.1007/978-3-319-06028-6_51

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06027-9

  • Online ISBN: 978-3-319-06028-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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