LSTM Based Semi-Supervised Attention Framework for Sentiment Analysis

With the rapid development of Internet technology and social media, people are accustomed to making comments on the Internet. Sentiment analysis, as an efficient technique, has been used by researchers in the tasks of analysing the sentiment polarity under these comments. To better achieve this target, the fundamental challenge is how to extract the feature and build a proper mechanism to learn them. A lot of word embedding based deep learning models for sentiment analysis are proposed in the literature. And the semi-supervised learning methods make it possible to use both labelled and unlabelled data for this kind of task. Furthermore, the attention mechanism proposed in recent years has achieved great accomplishments for natural language processing (NLP) tasks since it helps to capture the important information of the documents. In this paper, inspired by these works, we proposed a long short term memory (LSTM) based semi-supervised attention framework for sentiment analysis tasks, which is composed of an unsupervised attention based LSTM encoder-decoder and an attention based supervised LSTM model attached by a Softmax layer. The unsupervised part worked for attaining the high dimensional representation of the documents, and the supervised part extracted feature and enhanced the important parts for classification. Experimental study on commonly used datasets has demonstrated its ability for sentiment analysis tasks.