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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Accurate Retrieval of Corporate Reputation from Online Media Using Machine Learning

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DOI: http://dx.doi.org/10.15439/2019F169

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 4346 ()

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Abstract. Corporate reputation is an economic asset and its accurate measurement is of increasing interest in practice and science. This measurement task is difficult because reputation depends on numerous factors and stakeholders. Traditional measurement approaches have focused on human ratings and surveys, which are costly, can be conducted only infrequently and emphasize financial aspects of a corporation. Nowadays, online media with comments related to products, services, and corporations provides an abundant source for measuring reputation more comprehensively. Against this backdrop, we propose an information retrieval approach to automatically collect reputation-related text content from online media and analyze this content by machine learning-based sentiment analysis. We contribute an ontology for identifying corporations and a unique dataset of online media texts labelled by corporations' reputation. Our approach achieves an overall accuracy of 84.4\%. Our results help corporations to quickly identify their reputation from online media at low cost.


  1. A. Pharoah, “Corporate Reputation: The Boardroom Challenge,” Corp. Gov., vol. 3, no. 4, pp. 46–51, 2003. http://dx.doi.org/10.1108/14720700310497113
  2. H. Shefrin and M. Statman, “Making sense of beta, size and book-to-market,” J. Portfolio Manage., vol. 21, no. 2, pp. 26–34, 1995. http://dx.doi.org/10.3905/jpm.1995.409506
  3. H. Shefrin, “Do investors expect higher returns from safer stocks than from riskier stocks?,” J. Psychol. Financ. Market., vol. 2, no. 4, pp. 37–41, 2001. http://dx.doi.org/10.1207/S15327760JPFM0204_1
  4. D. MacGregor, P. Slovic, D. Dreman, and M. Berry, “Imagery, affect, and financial judgment,” J. Psychol. Financ. Market, vol. 1, no. 2, pp. 104–110, 2000. http://dx.doi.org/10.1207/S15327760JPFM0102_2
  5. S. Hammond and J. Slocum, “The impact of prior firm financial performance on subsequent corporate reputation,” J. Bus. Ethics, vol. 15, no. 2, pp. 159–165, 1996. https://doi.org/10.1007/BF00705584
  6. M. Sobol and G. Farrelly, “Corporate reputation: A function of relative size or financial performance,” Rev. Bus. Econ. Res., vol. 24, no. 1, pp. 45–59, 1988.
  7. P. Roberts and G. Dowling, “Corporate reputation and sustained superior financial performance,” Strateg. Manage. J., vol. 23, no. 12, pp. 1077–1093, 2002. http://dx.doi.org/10.1002/smj.274
  8. J. Bloemer, K. De Ruyter, and P. Peeters, “Investigating drivers of bank loyalty: the complex relationship between image, service quality and satisfaction,” Int. J. Bank. Market., vol. 16, no. 7, pp. 276–286, 1998. https://doi.org/10.1108/02652329810245984
  9. G. E. Fryxell and J. Wang, “The Fortune Corporate ‘Reputation’ Index: Reputation for What?,” J. Manage., vol. 20, no. 1, pp. 1–14, 1994. https://doi.org/10.1177/014920639402000101
  10. S. Brown, B., Perry, “Removing the Financial Performance Halo from Fortune’s ‘Most Admired’ Companies,” Acad. Manage. J., vol. 37, no. 5, pp. 1347–1359, 1994. https://doi.org/10.5465/256676
  11. V. Kubitscheck, “Business discontinuity – a risk too far,” Balance Sheet, vol. 9, no. 3, pp. 33–38, 2001. http://doi.org/10.1108/09657960110696032
  12. C. J. Fombrun and C. B. M. van Riel, “The Reputational Landscape,” Corporate Reputation Review, vol. 1, no. 1, pp. 5–13, 1997. https://doi.org/10.1057/palgrave.crr.1540008
  13. M. L. Barnett, J. M. Jermier, and B. Lafferty, “Corporate Reputation: The Definitional Landscape,” Corporate Reputation Review, vol. 9, no. 1, pp. 26–38, 2006. http://doi.org/10.1057/palgrave.crr.1550012
  14. T. J. Brown, P. A. Dacin, M. G. Pratt, and D. . Whetten, “Identity, Intended Image, Construed Image, and Reputation: An Interdisciplinary Framework and Suggested Terminology,” J. Acad. Market. Sci., vol. 34, no. 2, pp. 99–106, 2006. http://doi.org/10.1177/0092070305284969
  15. E. G. Love and M. Kraatz, “Character, Conformity, or the Bottom Line? How and Why Downsizing Affected Corporate Reputation,” Acad. Manage. J., vol. 52, no. 2, pp. 314–335, 2009. http://doi.org/10.5465/AMJ.2009.37308247
  16. D. Lange, P. M. Lee, and Y. Dai, “Organizational Reputation: A Review,” J. Manage., vol. 37, no. 1, pp. 153–184, 2010. http://doi.org/10.1177/0149206310390963
  17. P. Rhee, M., Haunschild, “The liability of good reputation: A study of product recalls in the US automobile industry,” Organization Science, vol. 17, no. 1, pp. 101–117, 2006. https://doi.org/10.1287/orsc.1050.0175
  18. D. Basdeo, K. Smith, C. M. Grimm, V. P. Rindova, and P. J. Derfus, “The impact of market actions on firm reputation. Strateg. Manage,” Strateg. Manage. J., vol. 27, no. 12, pp. 1205–1219, 2006. http://doi.org/10.1002/smj.556
  19. C. Fombrun and M. Shanley, “What’s in a Name? Reputation Building and Corporate Strategy,” Acad. Manage. J., vol. 33, no. 2, pp. 233–258, 1990. http://doi.org/10.2307/256324
  20. S. J. Brammer and S. Pavelin, “Corporate Reputation and Social Performance: The Importance of Fit,” Journal of Management Studies, vol. 43, no. 3, pp. 435–455, 2006. https://doi.org/10.1111/j.1467-6486.2006.00597.x
  21. C. Fombrun, “Corporate Reputation–its Measurement and Management,” Thexis, vol. 18, no. 4, pp. 23–26, 2001.
  22. D. Turban, D., Greening, “Corporate Social Performance and Organizational Attractiveness to prospective employees,” Acad. Manage. J., vol. 40, no. 3, pp. 658–672, 1997. https://doi.org/10.5465/257057
  23. D. Cable and M. Graham, “The determinants of job seekers’ reputation perceptions,” J. Organ. Behav., vol. 21, no. 8, pp. 929–947, 2000. https://doi.org/10.1002/1099-1379(200012)21:8<929::AID-JOB63>3.0.CO;2-O
  24. V. Rindova and I. Williamson, “Being good or being known: An empirical examination of the dimensions, antecedents, and consequences of organizational reputation,” Acad. Manage. J., vol. 48, no. 6, pp. 1033–1049, 2005. https://doi.org/10.5465/amj.2005.19573108
  25. G. Davies, R. Chun, and R. da Silva, “The personification metaphor as a measurement approach for corporate reputation,” Corporate Reputation Review, vol. 4, no. 2, pp. 113–127, 2001. https://doi.org/10.1057/palgrave.crr.1540137
  26. B. Liu and Zhang, “A survey of opinion mining and sentiment analysis,” in Mining Text Data, 2012, pp. 415–463. https://doi.org/10.1007/978-1-4614-3223-4_13
  27. A. Klein, O. Altuntas, T. Haeusser, and W. Kessler, “Extracting Investor Sentiment from Weblog Texts: A Knowledge-based Approach,” in 13th Conference on Commerce and Enterprise Computing IEEE, 2011, pp. 1–9. https://doi.org/10.1109/CEC.2011.10
  28. A. Klein, O. Altuntas, M. Riekert, and V. Dinev, “A Combined Approach for Extracting Financial Instrument-Specific Investor Sentiment from Weblogs,” in 11th International Conference on Wirtschaftsinformatik, 2013, pp. 691–705.
  29. F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, Mar. 2002. https://doi.org/10.1145/505282.505283
  30. T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” in 10th European Conference on Machine Learning, 1998, vol. 1398, no. 2, pp. 137–142. https://doi.org/10.1007/BFb0026683
  31. D. Maynard et al., “Architectural elements of language engineering robustness,” Natural Language Engineering, vol. 8, pp. 257–274, 2002. https://doi.org/10.1017/S1351324902002930
  32. N. O’Hare et al., “Topic-Dependent Sentiment Analysis of Financial Blogs,” in International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, 2009, pp. 9–16. https://doi.org/10.1145/1651461.1651464
  33. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” in Conference on Empirical Methods in Natural Language Processing, 2002, pp. 79–86. https://doi.org/10.3115/1118693.1118704
  34. M. Riekert, J. Leukel, and A. Klein, “Online Media Sentiment: Understanding Machine Learning-Based Classifiers,” Proceedings of the 24th European Conference on Information Systems (ECIS), 2016.
  35. H. Tang, S. Tan, and X. Cheng, “A survey on sentiment detection of reviews,” Expert Systems with Applications, vol. 36, no. 7, pp. 10760–10773, Sep. 2009. https://doi.org/10.1016/j.eswa.2009.02.063
  36. J. L. Fleiss, “Measuring nominal scale agreement among many raters,” Psychological bulletin, vol. 76, no. 5, 1971. http://doi.org/10.1037/h0031619
  37. B. Efron, “Estimating the error rate of a prediction rule: improvement on cross-validation,” Journal of the American Statistical Association, vol. 78, no. 382, pp. 316–331, 1983. https://doi.org/10.2307/2288636
  38. R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995, pp. 1137–1143.
  39. Y. Yang, “An evaluation of statistical approaches to text categorization,” Information retrieval, vol. 1, no. 1–2, pp. 69–90, 1999. https://doi.org/10.1023/A:1009982220290
  40. R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Systems with Applications, vol. 40, no. 2, pp. 621–633, Feb. 2013. https://doi.org/10.1016/j.eswa.2012.07.059