Deep Recurrent Neural Model for Multi Domain Sentiment Analysis with Attention Mechanism

The problem of multi-domain sentiment analysis is complex since meaning of words in different domains can be interpreted differently. This paper proposes a deep bi-directional Recurrent Neural Network based sentiment classification system employing attention mechanism for multi-domain classifications. The approach derives domain representation by extracting features related to description of domain from the text using bidirectional recurrent network with attention and feed it to the sentiment classifier along with the processed text using common hidden layers. We experiment with varied types of recurrent networks and propose that implementing the recurrent network with gated recurrent unit ensures that both domain-specific feature extraction and feature sharing for classification can be performed simultaneously and effectively. The evaluation of domain and sentiment modules has been conducted separately and results are encouraging. We found that using gated recurrent unit as bidirectional recurrent network in both modules gives efficient performance as it trains quickly and gives higher validation accuracy for all present domains. The proposed model also demonstrated good results for other metrics when compared with other similar state-of-the-art approaches.