Attention Aware Semi-supervised Framework for Sentiment Analysis

Using sentiment analysis methods to retrieve useful information from the accumulated documents in the Internet has become an important research subject. In this paper, we proposed a semi-supervised framework, which uses the unlabeled data to promote the learning ability of the long short memory (LSTM) network. It is composed of an unsupervised attention aware long short term memory (LSTM) encoder-decoder and a single LSTM model used for feature extraction and classification. Experimental study on commonly used datasets has demonstrated our framework's good potential for sentiment classification tasks. And it has shown that the unsupervised learning part can improve the LSTM network's learning ability.