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Using collaborative tagging for text classification: from text classification to opinion mining

Eric Charton, Marie-Jean Meurs, Ludovic Jean-Louis and Michel Gagnon

Article (2013)

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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.

Uncontrolled Keywords

text classification, opinion mining, collaborative corpus, collaborative tagging, machine learning

Subjects: 2700 Information technology > 2706 Software engineering
2700 Information technology > 2709 Other computing methods
Department: Department of Computer Engineering and Software Engineering
Funders: Wikimeta Technologies Inc., Genome Canada - Genozymes Project, Genome Québec - Genozymes Project
PolyPublie URL: https://publications.polymtl.ca/3633/
Journal Title: Informatics (vol. 1, no. 1)
Publisher: MDPI
DOI: 10.3390/informatics1010032
Official URL: https://doi.org/10.3390/informatics1010032
Date Deposited: 04 Feb 2019 10:19
Last Modified: 05 Apr 2024 14:05
Cite in APA 7: Charton, E., Meurs, M.-J., Jean-Louis, L., & Gagnon, M. (2013). Using collaborative tagging for text classification: from text classification to opinion mining. Informatics, 1(1), 32-51. https://doi.org/10.3390/informatics1010032

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