Sentiment Analysis of Monkeypox Tweets Using a Hybrid CNN-BiGRU Based Deep Learning Model

Recently, epidemics have disrupted people's lives. Following the COVID-19 pandemic, cases of monkeypox, a rare disease, began to appear worldwide. These incidents raised societal tension and anxiety. Understanding public attitudes, sentiments, and perceptions regarding the epidemic is essential for developing and implementing effective policies and countermeasures. Online social media platforms are essential venues for expressing feelings and views. In particular, Twitter provides valuable information for measuring societal sentiment. Machine learning and deep learning methods have recently been widely used in social network analysis. Deep learning models, which deliver strong performance, particularly on complex and nonlinear problems, are used in natural language processing and text mining. In this study, a Convolutional Neural Network Bidirectional Gated Recurrent Unit (CNN-BiGRU) hybrid deep learning model was developed by combining a CNN and a BiGRU and applied for the monkeypox sentiment analysis. The dataset was labeled as positive, negative, or neutral with Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, FastText and Term Frequency-Inverse Document Frequency (TF-IDF) word-embedding methods were used for feature extraction, and the performance of machine-learning and deep-learning methods was evaluated separately. As a result of the experimental studies, the FastText-CNN-BiGRU model obtained the highest results with 0.9382 accuracy, 0.9347 macro precision, 0.9367 macro recall, 0.9355 macro F-score, and 0.9875 macro ROC-AUC. The FastText-CNN model was the second most successful, achieving 0.9303 accuracy, 0.9268 macro precision, 0.9280 macro recall, 0.9271 macro F-score, and 0.9862 macro ROC-AUC. The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.