Journal
INFORMATION SCIENCES
Volume 355, Issue -, Pages 348-365Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.02.002
Keywords
Tweet sentiment analysis; Semi-supervised learning
Categories
Funding
- Capes [DS-7253238/D]
- CNPq [303348/2013-5]
- FAPESP [2013/07375-0, 2010/20830-0]
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Supervised algorithms require a set of representative labeled data for building classification models. However, labeled data are usually difficult and expensive to obtain, which motivates the interest in semi-supervised learning. This type of learning uses both labeled and unlabeled data in the training process and is particularly useful in applications such as tweet sentiment analysis, where a large amount of unlabeled data is available. Semi supervised learning for tweet sentiment analysis, although quite appealing, is relatively new. We propose a semi-supervised learning framework that combines unsupervised information, captured from a similarity matrix constructed from unlabeled data, with a classifier. Our motivation is that such a similarity matrix is a powerful knowledge-discovery tool that can help classify unlabeled tweet sets. Our framework makes use of the well-known Self-training algorithm to induce a better tweet sentiment classifier. Experimental results in real-world datasets demonstrate that the proposed framework can improve the accuracy of tweet sentiment analysis. (C) 2016 Elsevier Inc. All rights reserved.
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