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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9103)


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.


RepLab Real-time Database Reputation Management Tweets Variable Alert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LIA/Université d’Avignon et des Pays de VaucluseAvignon cedex 9France

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