Structural information aware deep semi-supervised recurrent neural network for sentiment analysis

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• 作者：Wenge Rong ; Baolin Peng ; Yuanxin Ouyang ; Chao Li…
• 关键词：sentiment analysis ; recurrent neural network ; deep learning ; machine learning
• 刊名：Frontiers of Computer Science in China
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：9
• 期：2
• 页码：171-184
• 全文大小：634 KB
• 参考文献：1. Tan, C, Lee, L, Tang, J, Jiang, L, Zhou, M, Li, P (2011) User-level sentiment analysis incorporating social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1397-1405
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• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to understand the polarity beneath these comments. Currently a lot of approaches from natural language processing’s perspective have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analysis methods. In this research, we further investigate the structural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is playing important role in identifying the overall statement’s polarity. As a result a novel sentiment analysis model is proposed based on recurrent neural network, which takes the partial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.