Using BiLSTM Structure with Cascaded Attention Fusion Model for Sentiment Analysis

Abstract

In the last decade, sentiment analysis has been a popular research area in the domains of natural language processing and data mining. Sentiment analysis has several commercial and social applications. The technique is essential to analyse the customer experience to develop customer loyalty and maintenance through better assistance. Deep Neural Network (DNN) models have recently been used to do sentiment analysis tasks with promising results. The disadvantage of such models is that they value all characteristics equally. We propose a Cascaded Attention Fusion Model-based BiLSTM to address these issues (CAFM-BiLSTM). Multiple heads with embedding and BiLSTM layers are concatenated in the proposed CAFM-BiLSTM. The information from both deep multi-layers is merged and provided as input to the BiLSTM layer later in this paper. The results of our fusion model are superior to those of the existing models. Our model outperforms the competition for lengthier sentence sequences and pays special attention to referral words. The accuracy of the proposed CAFM-BiLSTM is 5.1%, 5.25%, 6.1%, 12.2%, and 13.7% better than RNN-LSTM, SVM, NB, RF and DT respectively.