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Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining
Ecole Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada
Centre for Structural and Functional Genomics, Concordia University, Montréal,QC H4B 1R6, Canada
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Received: 24 September 2013; in revised form: 11 November 2013 / Accepted: 21 November 2013 / Published: 28 November 2013
Abstract: Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, related to the classification of cooking recipes tagged by users from a collaborative website. This context makes some of the corpus specificities difficult to model for machine-learning-based systems and keyword or lexical-based systems. In particular, different authors might have different opinions on how to classify a given document. The systems presented hereafter were submitted to the D´Efi Fouille de Textes 2013 evaluation campaign, where they obtained the best overall results, ranking first on task 1 and second on task 2. In this paper, we explain our approach for building relevant and effective systems dealing with such a corpus.
Keywords: text classification; opinion mining; collaborative corpus; collaborative tagging; machine learning
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MDPI and ACS Style
Charton, E.; Meurs, M.-J.; Jean-Louis, L.; Gagnon, M. Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining. Informatics 2014, 1, 32-51.
Charton E, Meurs M-J, Jean-Louis L, Gagnon M. Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining. Informatics. 2014; 1(1):32-51.
Charton, Eric; Meurs, Marie-Jean; Jean-Louis, Ludovic; Gagnon, Michel. 2014. "Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining." Informatics 1, no. 1: 32-51.