Abstract
In this paper, we address the task of detecting the reputation alert in social media updates, that is, deciding whether a new-coming content has strong and immediate implications for the reputation of a given entity. This content is also submitted to a standard typology of reputation dimensions that consists in a broad classification of the aspects of an under public audience company. Reputation manager needs a real-time database and method to report what is happening right now to his brand. However, typical Natural Language Processing (NLP) approaches to these tasks require external resources and show non-relational modeling. We propose a fast supervised approach for extracting textual features, which we use to train simple statistical reputation classifiers. These classifiers outputs are used in a Partial Least Squares Path Modeling (PLS-PM) system to model the reputation. Experiments on the RepLab 2013 and 2014 collections show that our approaches perform as well as the state-of-the-art more complex methods.
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Notes
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Replab provides a framework to evaluate Online Reputation Management systems on Twitter http://www.limosine-project.eu/events/replab2013.
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Reputation Institute’s Reptrak framework http://www.reputationinstitute.com/about-reputation-institute/the-reptrak-framework.
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We remove links, stop-words and punctuation marks.
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This work is funded by the project ImagiWeb ANR-2012-CORD-002-01.
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Cossu, JV., Sanjuan, E., Torres-Moreno, JM., El-Bèze, M. (2015). Automatic Classification and PLS-PM Modeling for Profiling Reputation of Corporate Entities on Twitter. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_25
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