Using unsupervised information to improve semi-supervised tweet sentiment classification

• Published in: Information sciences Vol. 355-356; pp. 348 - 365
• Main Authors: da Silva, Nádia Félix Felipe, Coletta, Luiz F.S., Hruschka, Eduardo R., Hruschka Jr, Estevam R.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 10.08.2016
• Subjects: Algorithms; Buildings; Classification; Classifiers; Data mining; Learning; Semi-supervised learning; Similarity; Training; Tweet sentiment analysis; Semi-supervised learning; Tweet sentiment analysis
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2016.02.002

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.