Hybrid evolutionary intelligent network for sentiment analysis using Twitter data during COVID-19 pandemic

Abstract

COVID-19 pandemic has impacted many nations, causing physical as well as mental health concerns globally. In most countries, governments enforced strict lockdowns and social distancing, thus affecting people's daily lives. People usually tweet their views on online platforms that is unstructured text with implicit meaning. With the evolution of artificial intelligence in the natural language processing domain, the prediction of sentiments accurately has become a challenge. To contribute as a solution to this, a hybrid approach is proposed for sentiment prediction with the use of an evolutionary-based approach, transfer-based learning and machine learning. The proposed approach uses bidirectional encoder representations from transformers (BERT) with genetic algorithm (GA) and support vector machine (SVM), namely, hybrid evolutionary intelligent model (GA-BERT-SVM). These approaches aid in extracting important features considering semantics and context present in the text. To avoid the limitations of the backpropagation approach, such as trapping in local minima and overfitting the data, the initial parameters (weights and biases) of the dense layers has been optimized using GA. Additionally, the pretrained BERT layers are utilized without any modification, following a standard transfer learning approach. The BERT embeddings are concatenated with the SVM for training and classification. GridSearchCV and GeneticSearchCV is used for obtaining optimal parameters of SVM. A multi-classification problem is tackled using a benchmark COVID-19 dataset, which comprises of Twitter data and is categorized into COVIDSENTI-A, COVIDSENTI-B, COVIDSENTI-C and a combined dataset called COVIDSENTI. Experimental evaluation demonstrates promising results of the proposed model in terms of accuracy, F1-score, precision and recall, surpassing state-of-the-art approaches.