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Automatic Classification and PLS-PM Modeling for Profiling Reputation of Corporate Entities on Twitter

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

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

  1. 1.

    Replab provides a framework to evaluate Online Reputation Management systems on Twitter http://www.limosine-project.eu/events/replab2013.

  2. 2.

    See [13] http://www.cs.cornell.edu/people/tj/svm_light/svm_multiclass.html.

  3. 3.

    Reputation Institute’s Reptrak framework http://www.reputationinstitute.com/about-reputation-institute/the-reptrak-framework.

  4. 4.

    We remove links, stop-words and punctuation marks.

  5. 5.

    http://cran.r-project.org/web/packages/plspm/.

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Acknowledgment

This work is funded by the project ImagiWeb ANR-2012-CORD-002-01.

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Correspondence to Jean-Valère Cossu .

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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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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_25

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