Document-level sentiment analysis using Jaya chimp optimisation algorithm-enabled deep residual network

Document-level sentiment classification automates the process of categorising text reviews on a single topic as representing negative or positive sentiments. Users and customers are intended to share comments and reviews about their products on various social network sites. One of these processing steps is the classification of emotions associated with the reviews. Therefore, this research paper introduces a robust sentiment analysis method, named Jaya chimp optimisation algorithm-enabled deep residual network (JayaChOA-enabled DRN) for document-level sentiment classification. The input is pre-processed and tokenised, and then the key features are extracted. Moreover, the DRN classifier is used for the sentiment classification where the optimal weights are computed using the JayaChOA. Meanwhile, the introduced JayaChOA is implemented by the incorporation of Jaya optimiser and chimp optimisation algorithm (ChOA). The JayaChOA-based DRN obtained the highest precision of 0.914, F-measure of 0.919, and recall of 0.925 using K-fold.