Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining

• Published in: Informatics (Basel) Vol. 1; no. 1; pp. 32 - 51
• Main Authors: Charton, Eric, Meurs, Marie-Jean, Jean-Louis, Ludovic, Gagnon, Michel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2014
• Subjects: Classification; collaborative corpus; collaborative tagging; Construction; Data mining; Dealing; Heating; machine learning; opinion mining; Platforms; Tasks; text classification; Texts
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics1010032

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.