期刊
JOURNAL OF VISUAL LANGUAGES AND COMPUTING
卷 25, 期 6, 页码 840-849出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jvlc.2014.09.005
关键词
Sentiment analysis; Bagging; Auto-encoder
资金
- National Natural Science Foundation of China [61332018]
- National High Technology Research and Development Program of China [2013AA01A601]
- Fundamental Research Funds for the Central Universities
- Shenzhen Key Laboratory of Data Vitalization (Smart City)
Sentiment analysis has long been a hot topic for understanding users statements online. Previously many machine learning approaches for sentiment analysis such as simple feature-oriented SVM or more complicated probabilistic models have been proposed. Though they have demonstrated capability in polarity detection, there exist one challenge called the curse of dimensionality due to the high dimensional nature of text-based documents. In this research, inspired by the dimensionality reduction and feature extraction capability of auto-encoders, an auto-encoder-based bagging prediction architecture (AEBPA) is proposed. The experimental study on commonly used datasets has shown its potential. It is believed that this method can offer the researchers in the community further insight into bagging oriented solution for sentimental analysis. (C) 2014 Elsevier Ltd. All rights reserved.
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