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Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

Authors :
Yang Li
Ying Lv
Suge Wang
Jiye Liang
Juanzi Li
Xiaoli Li
Source :
Symmetry, Vol 11, Iss 2, p 133 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consuming task. Therefore, based on the idea of effectively utilizing unlabeled samples, a synthetical framework that covers the whole process of semi-supervised learning from seed selection, iterative modification of the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification. To provide an important basis for selecting the seed texts and modifying the training text set, three kinds of measures—the cluster similarity degree of an unlabeled text, the cluster uncertainty degree of a pseudo-label text to a learner, and the reliability degree of a pseudo-label text to a learner—are defined. With these measures, a seed selection method based on Random Swap clustering, a hybrid modification method of the training text set based on active learning and self-learning, and an alternately co-training strategy of the ensemble classifier of the Maximum Entropy and Support Vector Machine are proposed and combined into our framework. The experimental results on three Chinese datasets (COAE2014, COAE2015, and a Hotel review, respectively) and five English datasets (Books, DVD, Electronics, Kitchen, and MR, respectively) in the real world verify the effectiveness of the proposed framework.

Details

Language :
English
ISSN :
20738994
Volume :
11
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Symmetry
Publication Type :
Academic Journal
Accession number :
edsdoj.3d11c5822e2246d2a6843cb613216412
Document Type :
article
Full Text :
https://doi.org/10.3390/sym11020133