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

Informatics

Eric Charton²  Marie-Jean Meurs¹  Ludovic Jean-Louis² 

[1] Centre for Structural and Functional Genomics, Concordia University, Montréal, QC H4B 1R6, Canada;Ecole Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada; E-Mails:

关键词: text classification;    opinion mining;    collaborative corpus;    collaborative tagging;    machine learning;   

DOI  :  10.3390/informatics1010032

来源: mdpi

【 摘 要 】

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 DÉfi 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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.