Using unsupervised information to improve semi-supervised tweet sentiment classification

Author(s)

Felipe da Silva, Nadia Felix ^[1] ; Coletta, Luiz F. S. ^[1] ; Hruschka, Eduardo R. ^[1] ; Hruschka, Jr., Estevam R. ^[2]

Affiliation

^[1] Univ Sao Paulo, Dept Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13560970 Sao Carlos, SP - Brazil

^[2] Fed Univ UFSCAR, Dept Comp Sci, Rodovia Washington Luis, Km 235-SP-310, BR-13565905 Sao Carlos, SP - Brazil

• Document type: Journal article
• Source: INFORMATION SCIENCES; v. 355, p. 348-365, AUG 10 2016.
• Web of Science Citations: 13

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. (C) 2016 Elsevier Inc. All rights reserved. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 10/20830-0 - Evolutionary Algorithms for Aggregating Ensembles of Classifiers and Clusterers
• Grantee: Luiz Fernando Sommaggio Coletta
• Support Opportunities: Scholarships in Brazil - Doctorate