Auto-encoder based bagging architecture for sentiment analysis

• DOI: 10.1016/j.jvlc.2014.09.005
• Publication Date: 2014-10-08T03:42:09Z
• Authors: Wenge Rong; Yifan Nie; Yuanxin Ouyang; Baolin Peng; Zhang Xiong
• Keywords: Sentiment Analysis; Polarity (international relations); Feature (linguistics)

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. HighlightsThis research addresses the curse of dimensionality challenge by employing auto-encoders.Bagging method is employed to further improve performance.An auto-encoder-based bagging architecture is proposed and experiment on real-world datasets reveals its potential.