Microblog sentiment analysis based on collaborative learning under loose conditions
SUN Nian1, LI Yu-qiang1, LIU Ai-hua2, LIU Chun1, LI Wei-wei1
1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China;
2. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Aiming at the facts that two completely redundant feature views are required in the traditional collaborative learning and the redundancy of features are not reached in most cases, a collaborative learning framework under loose conditions was proposed. The support vector machine algorithm and the long short-term memory algorithm were used to establish the microblog feature view based on the vector model and the word vector model. The collaborative learning was conducted on these two models. A new selection strategy of unmarked samples which combined the uncertain strategy in active learning and the maximum certainty-factor was proposed. The information contained in unlabeled samples was fully used. The experimental results show that compared with the traditional selection strategy, the selection strategy improves the quality of categorizer and manages to complete Chinese microblog sentiment analysis with the proposed collaborative learning framework under loose conditions.
Received: 14 November 2017
Published: 23 August 2018
SUN Nian, LI Yu-qiang, LIU Ai-hua, LIU Chun, LI Wei-wei. Microblog sentiment analysis based on collaborative learning under loose conditions. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(8): 1452-1460.
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