Deep learning-based hybrid sentiment analysis with feature selection using optimization algorithm

In the past few years, sentiment analysis (SA) of online content has gained more attention in the research area due to the enormous increase of online content from various sources like websites, social blogs, etc. Many organizations use SA techniques to determine the opinion of users and to ensure their satisfaction. Numerous techniques are suggested by many researchers to identify the sentiments of online content. Among them, hybrid of deep learning and lexicon-based SA techniques are gaining more attention due to their outstanding performance than other approaches. Though the lexicon-based SA approaches integrated with deep learning SA approaches possess more advantages they suffer from lack of accuracy and scalability issues due to the high-dimensional features. To eliminate this issue, a hybrid SA approach is proposed in this paper with a bio-inspired feature selection technique. The Valence Aware Dictionary for Sentiment Reasoning (VADER) approach is integrated with the hybrid deep learning approach of attention-based bidirectional long short-term memory and variable pooling convolutional neural network (VPCNN-ABiLSTM) for SA. The optimal features are selected to minimize the scalability issue by integrating the chimp optimization algorithm with the opposition-based learning technique. The performance of the proposed approach is evaluated for four types of benchmark datasets in terms of precision, accuracy, recall, and F1 score. The proposed approach with OBL-CHOA based feature selection technique achieved higher accuracy of 97.1% with the reduction of 13.6% features. The accuracy of the proposed approach with the feature selection technique is 6.9% higher than the existing BiLSTM-CNN based SA approach.