| Texto completo | |
| Autor(es): |
Felipe da Silva, Nadia Felix
[1]
;
Coletta, Luiz F. S.
[1]
;
Hruschka, Eduardo R.
[1]
;
Hruschka, Jr., Estevam R.
[2]
Número total de Autores: 4
|
| Afiliação do(s) autor(es): | [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
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INFORMATION SCIENCES; v. 355, p. 348-365, AUG 10 2016. |
| Citações Web of Science: | 13 |
| Resumo | |
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) | |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 10/20830-0 - Algoritmos Evolutivos para Agregar Classificadores e Agrupadores |
| Beneficiário: | Luiz Fernando Sommaggio Coletta |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |