Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10358785 | Journal of Visual Languages & Computing | 2014 | 10 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Wenge Rong, Yifan Nie, Yuanxin Ouyang, Baolin Peng, Zhang Xiong,