Text Classification in Organizational Research – A Hybrid Approach Combining Dictionary Content Analysis and Supervised Machine Learning Techniques

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Cover of Volume: mrev management revue Volume 33 (2022), Issue 1
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mrev management revue

Volume 33 (2022), Issue 1

Authors:
Publisher
Nomos, Baden-Baden
Copyright Year
2022
ISSN-Online
1861-9908
ISSN-Print
0935-9915

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Open Access Full access

Volume 33 (2022), Issue 1

Text Classification in Organizational Research – A Hybrid Approach Combining Dictionary Content Analysis and Supervised Machine Learning Techniques

Authors:
ISSN-Print
0935-9915
ISSN-Online
1861-9908
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Bibliography

  1. Agarwal, S., & Sureka, A. (2016). But i did not mean it!—intent classification of racist posts on tumblr. Paper presented at the 2016 European Intelligence and Security Informatics Conference (EISIC). Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  2. Aggarwal, C. C. (2018). Machine learning for text. New York: Springer. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  3. Ahmad, K., Han, J., Hutson, E., Kearney, C., & Liu, S. (2016). Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance, 37, 152–172. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  4. Andal-Ancion, A., Cartwright, P. A., & Yip, G. S. (2003). The digital transformation of traditional business. MIT Sloan Management Review, 44(4), 34–41. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  5. Baccianella, S., Esuli, A., & Sebastiani, F. (2010). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Paper presented at the Lrec. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  6. Bae, Y., & Lee, H. (2012). Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the American society for information science and technology, 63(12), 2521–2535. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  7. Bannier, C., Pauls, T., & Walter, A. (2019). Content analysis of business communication: introducing a German dictionary. Journal of Business Economics, 89(1), 79–123. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  8. Bansal, P., & Gao, J. (2006). Building the future by looking to the past: Examining research published on organizations and environment. Organization & environment, 19(4), 458–478. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  9. Bednar, M. K. (2012). Watchdog or lapdog? A behavioral view of the media as a corporate governance mechanism. Academy of Management Journal, 55(1), 131–150. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  10. Bernard, H. R., Wutich, A., & Ryan, G. W. (2016). Analyzing qualitative data: Systematic approaches: SAGE publications. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  11. Bonfiglioli, R., & Nanni, F. (2015). From close to distant and back: how to read with the help of machines. Paper presented at the 3rd International Conference on the History and Philosophy of Computing, Pisa, Italy. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  12. Caserio, C., Panaro, D., & Trucco, S. (2019). Management discussion and analysis: a tone analysis on US financial listed companies. Management Decision, 58(3), 510–525. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  13. Castelló, I., Etter, M., & Årup Nielsen, F. (2016). Strategies of legitimacy through social media: The networked strategy. Journal of management studies, 53(3), 402–432. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  14. Chan, S. W., & Chong, M. W. (2017). Sentiment analysis in financial texts. Decision Support Systems, 94(2), 53–64. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  15. Cogburn, D., & Hine, M. (2017). Introduction to Text Mining in Big Data Analytics Minitrack. Paper presented at the Proceedings of the 50th Hawaii International Conference on System Sciences, HICSS 2017. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  16. Durand, R., & Vergne, J. P. (2015). Asset divestment as a response to media attacks in stigmatized industries. Strategic Management Journal, 36(8), 1205–1223. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  17. Duriau, V. J., Reger, R. K., & Pfarrer, M. D. (2007). A content analysis of the content analysis literature in organization studies: Research themes, data sources, and methodological refinements. Organizational research methods, 10(1), 5–34. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  18. Evans, M., McIntosh, W., Lin, J., & Cates, C. (2007). Recounting the courts? Applying automated content analysis to enhance empirical legal research. Journal of Empirical Legal Studies, 4(4), 1007–1039. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  19. Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data: Cambridge university press. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  20. Garrad, M. W. (2003). Computer Aided Text Analysis in Personnel Selection. PhD-thesis, School of Applied Psychology, Griffith University, Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  21. George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research. Academy of Management Journal, 59(5), 1493–1507. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  22. Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16), 6266–6282. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  23. Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332–359. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  24. HaCohen-Kerner, Y., Miller, D., & Yigal, Y. (2020). The influence of preprocessing on text classification using a bag-of-words representation. PloS one, 15(5), 1–22. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  25. Harish, B. S., Guru, D. S., & Manjunath, S. (2010). Representation and classification of text documents: A brief review. IJCA, Special Issue on RTIPPR (2), 110–119. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  26. Hausladen, C. I., Schubert, M. H., & Ash, E. (2020). Text classification of ideological direction in judicial opinions. International Review of Law and Economics, 62, 1–19. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  27. Heitmann, M., Siebert, C., Hartmann, J., & Schamp, C. (2020). More than a feeling: Benchmarks for sentiment analysis accuracy. Ssrn, Working Paper. Retrieved 29.3.2021, from https://dx.doi.org/10.2139/ssrn.3489963. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  28. Holsti, O. R. (1969). Content analysis for the social sciences and humanities. Reading, MA: Addison-Wesley Publishing Company. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  29. Holton, C. (2009). Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems, 46(4), 853–864. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  30. Hossfeld, H. (2013). Corporate dieting. Persuasive use of metaphors in downsizing. management revue, 24(1), 53–70. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  31. Hossfeld, H. (2018). Legitimation and institutionalization of managerial practices. The role of organizational rhetoric. Scandinavian Journal of Management, 34(1), 9–21. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  32. Huang, A. H., Zang, A. Y., & Zheng, R. (2014). Evidence on the information content of text in analyst reports. The Accounting Review, 89(6), 2151–2180. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  33. Humphreys, A., & Wang, R. J.-H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274–1306. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  34. Ignatow, G., & Mihalcea, R. (2016). Text mining: A guidebook for the social sciences. Thousan Oaks, California: Sage Publications. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  35. Ikonomakis, M., Kotsiantis, S., & Tampakas, V. (2005). Text classification using machine learning techniques. WSEAS transactions on computers, 4(8), 966–974. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  36. Kabanoff, B. (1997). Introduction: computers can read as well as count: computer-aided text analysis in organizational research. Journal of Organizational behavior, 18, 507–511. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  37. Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihók, G., & Den Hartog, D. N. (2018a). Text classification for organizational researchers: A tutorial. Organizational research methods, 21(3), 766–799. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  38. Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihók, G., & Den Hartog, D. N. (2018b). Text mining in organizational research. Organizational research methods, 21(3), 733–765. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  39. Kothari, S. P., Li, X., & Short, J. E. (2009). The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: A study using content analysis. The Accounting Review, 84(5), 1639–1670. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  40. Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American political science review, 97(2), 311–331. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  41. Laver, M., & Garry, J. (2000). Estimating policy positions from political texts. American Journal of Political Science, 619–634. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  42. Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of broadcasting & electronic media, 57(1), 34–52. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  43. Loughran, T., & McDonald, B. (2015). The use of word lists in textual analysis. Journal of Behavioral Finance, 16(1), 1–11. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  44. Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187–1230. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  45. Martin, A. D., Quinn, K. M., Ruger, T. W., & Kim, P. T. (2004). Competing approaches to predicting supreme court decision making. Perspectives on Politics, 2(4), 761–767. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  46. Park, J. r., Lu, C., & Marion, L. (2009). Cataloging professionals in the digital environment: A content analysis of job descriptions. Journal of the American society for information science and technology, 60(4), 844–857. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  47. Pencle, N., & Mălăescu, I. (2016). What's in the words? Development and validation of a multidimensional dictionary for CSR and application using prospectuses. Journal of Emerging Technologies in Accounting, 13(2), 109–127. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  48. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. Retrieved 29.3.2021, from https://mcrc.journalism.wisc.edu/files/2018/04/Manual_LIWC.pdf. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  49. Pérez-Vera, S., Alfaro, R., & Allende-Cid, H. (2017). Intent classification of social media texts with machine learning for customer service improvement. Paper presented at the International Conference on Social Computing and Social Media. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  50. Pfeffer, J., & Gerald, R. (1978). The external control of organizations: A resource dependence perspective: New York: Harper & Row. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  51. Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 1–24. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  52. Platanou, K., Mäkelä, K., Beletskiy, A., & Colicev, A. (2018). Using online data and network-based text analysis in HRM research. Journal of Organizational Effectiveness: People and Performance. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  53. Pollach, I. (2012). Taming textual data: The contribution of corpus linguistics to computer-aided text analysis. Organizational research methods, 15(2), 263–287. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  54. Prüfer, J., & Prüfer, P. (2018). Data science for institutional and organizational economics. In C. a. M. M. S. Ménard (Ed.), A research agenda for new institutional economics (pp. 248–259). Cheltenham: Edward Elgar Publishing. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  55. Riffe, D., Lacy, S., Fico, F., & Watson, B. (2019). Analyzing media messages: Using quantitative content analysis in research: Routledge. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  56. Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., & Sedlmair, M. (2018). More than bags of words: Sentiment analysis with word embeddings. Communication Methods and Measures, 12(2–3), 140–157. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  57. Scherer, A. G., & Palazzo, G. (2011). The new political role of business in a globalized world: A review of a new perspective on CSR and its implications for the firm, governance, and democracy. Journal of management studies, 48(4), 899–931. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  58. Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97–112. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  59. Strycharz, J., Strauss, N., & Trilling, D. (2018). The role of media coverage in explaining stock market fluctuations: Insights for strategic financial communication. International Journal of Strategic Communication, 12(1), 67–85. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  60. Stulpe, A., & Lemke, M. (2016). Blended reading. In M. Lemke & G. Wiedemann (Eds.), Text Mining in den Sozialwissenschaften (pp. 17–61). Wiesbaden: Springer. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  61. Thomson Reuters (2017). Thomson Reuters ESG Scores. Retrieved 29.3.2021, from https://www.esade.edu/itemsweb/biblioteca/bbdd/inbbdd/archivos/Thomson_Reuters_ESG_Scores.pdf. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  62. Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational research methods, 21(3), 525–547. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  63. Vaara, E., & Tienari, J. (2002). Justification, legitimization and naturalization of mergers and acquisitions: A critical discourse analysis of media texts. Organization, 9(2), 275–304. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  64. van Zoonen, W., & Toni, G. (2016). Social media research: The application of supervised machine learning in organizational communication research. Computers in Human Behavior, 63, 132–141. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  65. van Zoonen, W., Verhoeven, J. W., & Vliegenthart, R. (2016). How employees use Twitter to talk about work: A typology of work-related tweets. Computers in Human Behavior, 55, 329–339. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  66. Watanabe, K. (2016). Computer-aided dictionary making: An efficient dictionary construction technique for content analysis. Paper presented at the Proc. Int. Conf. on the Advances in Computational Analysis of Political Text. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  67. Watanabe, K. (2020). Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages. Communication Methods and Measures, 1–22. Retrieved 29.3.2021, from https://doi.org/10.1080/19312458.2020.1832976. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  68. Wenzel, R., & Van Quaquebeke, N. (2018). The double-edged sword of big data in organizational and management research: A review of opportunities and risks. Organizational research methods, 21(3), 548–591. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  69. Xie, P., & Xing, E. (2018). A neural architecture for automated icd coding. Paper presented at the Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59
  70. Yehia, A. M., Ibrahim, L. F., & Abulkhair, M. F. (2016). Text mining and knowledge discovery from big data: challenges and promise. International Journal of Computer Science Issues (IJCSI), 13(3), 54. Open Google Scholar DOI: 10.5771/0935-9915-2022-1-59

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