文章基本信息

标题：Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining
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作者：Eric Charton ; Marie-Jean Meurs ; Ludovic Jean-Louis 等
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2014
卷号：1
期号：1
页码：32-51
DOI：10.3390/informatics1010032
语种：English
出版社：MDPI Publishing
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 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.
关键词：text classification; opinion mining; collaborative corpus; collaborative tagging; machine learning