Using unsupervised information to improve semi-supervised tweet sentiment classification

• DOI: 10.1016/j.ins.2016.02.002
• Publication Date: 2016-02-19T13:14:00Z
• Authors: Nádia Félix Felip...; Luiz F.S. Coletta; Eduardo R. Hruschka; Estevam R. Hrusch...
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract 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.