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Semi-supervised Micro-blog Sentiment Classification Method Combining Active Learning and Co-training |
Bi Qiumin1, Li Ming2, Zeng Zhiyong3 |
1. Faculty of Art and Communication, Kunming University of Science and Technology, Kunming 650093, China;
2. School of Information, Yunnan University of Finance and Economics, Kunming 650221, China;
3. Center of Information Management, Yunnan University of Finance and Economics, Kunming 650221, China |
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Abstract [Objective] Aimed at less labeled data and more unlabeled samples in micro-blog sentiment classification, a novel method is proposed. [Methods] Active learning is introduced into co-training, the method selects the most valuable ones from low confidence samples, then labels and adds them into training dataset, trains classifiers again. [Results] Experimental results show that classifiers have better performance in this way, and the accuracy is improved obviously. Especially when labeled data reaches 40%, the accuracy increases by about 5%. [Limitations] In the collaborative process, random feature subspace generation can not build two strong classifiers, so hypothesis are not fulfilled. [Conclusions] This method solves the defects of co-training after introducing active learning; the performance and accuracy of classifiers are enhanced.
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Received: 20 June 2014
Published: 12 February 2015
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