Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining
| Autor: | Marie-Jean Meurs, Eric Charton, Michel Gagnon, Ludovic Jean-Louis |
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| Rok vydání: | 2013 |
| Předmět: |
text classification
Information retrieval lcsh:T58.5-58.64 lcsh:Information technology Computer Networks and Communications Computer science business.industry Communication Sentiment analysis collaborative tagging Context (language use) Ranking (information retrieval) Task (project management) Human-Computer Interaction machine learning Text mining collaborative corpus opinion mining business |
| Zdroj: | Informatics, Vol 1, Iss 1, Pp 32-51 (2013) Informatics; Volume 1; Issue 1; Pages: 32-51 |
| ISSN: | 2227-9709 |
| DOI: | 10.3390/informatics1010032 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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