Attention Aware Bidirectional Gated Recurrent Unit Based Framework for Sentiment Analysis

Sentiment analysis is an effective technique and widely employed to analyze sentiment polarity of reviews and comments on the Internet. A lot of advanced methods have been developed to solve this task. In this paper, we propose an attention aware bidirectional GRU (Bi-GRU) framework to classify the sentiment polarity from the aspects of sentential-sequence modeling and word-feature seizing. It is composed of a pre-attention Bi-GRU to incorporate the complicated interaction between words by sentence modeling, and an attention layer to capture the keywords for sentiment apprehension. Afterward, a post-attention GRU is added to imitate the function of decoder, aiming to extract the predicted features conditioned on the above parts. Experimental study on commonly used datasets has demonstrated the proposed framework's potential for sentiment classification.