This article summarises the current research on sentiment analysis (SA) and the use of machine learning (ML), artificial intelligence (AI), and deep neural networks (DNN) in this field. It also discusses research gaps that need to be addressed to make SA systems and approaches more effective and usable by practitioners in various fields. Examples of research conducted in this field include the development of a SA model based on sequence-based Neural Networks, an ensemble deep learning language model to enhance sentiment analysis in social media applications, an SA approach for predicting public awareness of COVID-19 prevention measures, and a task-oriented, granularity-oriented, and methodology-oriented SA approach for English social media sites.
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