Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

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.

LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification

“…CASCT [29]: A co-operative hybrid semi-supervised learning for text sentiment classification Figure 15 along with table shows sentiment classification performance of LESSA against state-of-the-art approaches. It is clear that our proposed approach LESSA achieved the highest performance in terms of accuracy on the multi-domain datasets.…”

“…Yang et al [37] presented an LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification, combining the idea of lexicon-based learning and corpus-based learning in a unified co-training framework. Li et al [29] proposed a cooperative semi-supervised learning approach based on the hybrid mechanism of active learning and self-learning for textual sentiment classification.…”

“…In literature, for SA, SSL has been used by many researchers while gaining performance [29][30][31]41,51]. However, most of them did not notice how to utilize effectively concealed significant information, which gives more sentiment clue as compare to other words.…”

“…Semi-supervised learning (SSL) is a machine learning technique that utilizes a large amount of unlabeled data along with a small amount of labeled data to learn classifiers and achieve a better performance [27]. In the literature, mostly learning models uses semi-supervised learning techniques, such as self-training, co-training, active learning, and a combination of these has been mostly used for textual sentiment classification [28][29][30][31][32][33].…”

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

“…Ensemble/cooperative methods provide more accurate and robust solutions in comparison with individual techniques [33]. Cooperative clustering has largely been explored in various domains including software modularization [34], [35] and pattern recognition [36], text classification [37].…”