An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

Abstract

Sentiment Classification is a key area of research in natural language processing and it is frequently applied across a range of industries. The goal of Sentiment Analysis is to figure out if a product or service received a negative or positive response. Sentiment Analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach to evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short-Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the proposed model's performance. The results show that the proposed model has an enhanced accuracy of 97.01%, which is significantly better than existing models.