Abstract
This paper proposes a text classification method for identifying corporate social media users. With the explosion of social media content, it is imperative to have user identification tools to classify personal accounts from corporate ones. In this paper, we use text data from Twitter to demonstrate an efficient corporate user identification method. This method uses text classification with simple but robust processing. Our experiment results show that our method is lightweight, efficient and accurate.
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References
Argamon, S., Koppel, M., Pennebaker, J., Schler, J.: Automatically profiling the author of an anonymous text. Communications of the ACM 52(2), 119–123 (2009)
Bobicev, V., Sokolova, M., Jafer, Y., Schramm, D.: Learning sentiments from tweets with personal health information. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 37–48. Springer, Heidelberg (2012)
Estival, D., Gaustad, T., Pham, S., Radford, W., Hutchinson, B.: Tat: an author profiling tool with application to arabic emails. In: Proceedings of the Australasian Language Technology Workshop, pp. 21–30 (2007)
Ikeda, K., Hattori, G., Ono, C., Asoh, H., Higashino, T.: Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems 51, 35–47 (2013)
Khan, F., Bashir, S., Qamar, U.: Tom: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems (2013)
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Yang, Z., Wołkowicz, J., Kešelj, V. (2014). Social Media Corporate User Identification Using Text Classification. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_39
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DOI: https://doi.org/10.1007/978-3-319-06483-3_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06482-6
Online ISBN: 978-3-319-06483-3
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