An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques

Abstract

In the wake of the COVID-19 pandemic, social media platforms like Twitter have become critical channels for public expression, capturing a wide array of sentiments ranging from fear and anxiety to hope and optimism. This paper proposes an ensemble approach for automatic sentiment analysis of COVID-19-related tweets to extract valuable insights from large-scale data. The proposed method integrates multiple machine learning algorithms, including support vector machines (SVM), random forests, and deep learning models such as long short-term memory (LSTM) networks. By leveraging these diverse techniques, the ensemble model aims to improve classification accuracy and robustness in detecting positive, negative, and neutral sentiments. Feature extraction is optimized through natural language processing (NLP) techniques like term frequency-inverse document frequency (TF-IDF) and word embeddings. Experimental results on a publicly available COVID-19 Twitter dataset demonstrate the effectiveness of the proposed approach, showcasing its potential to contribute to public health monitoring, policy making, and understanding of public reactions during crises.