The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

互联网的快速发展已经达到了第四代，即Web 4.0，它在社交媒体、营销、风险管理、医疗保健、企业、网站、数据挖掘、电子学习、心理学，等等。情绪分析是政府、企业和研究人员分析用户情绪和心理状态的强大工具，以便生成有关产品、服务和日常活动的意见和评论。在过去的几年里，一些基于机器学习（ML）、深度学习（DL）和其他软计算方法的SA技术被提出。然而，不断增长的数据规模、主观性和多样性对提高现有技术的效率和纳入当前的发展趋势（例如多模态情感分析（MSA）和融合技术）提出了重大挑战。为了帮助热情的研究人员把握当前趋势，本文对各种文献进行了全面研究，以处理 SA 的不同方面，包括跨多个领域的当前趋势和技术。为了阐明 MSA 的未来前景，本文还强调了导致许多未解决挑战的开放问题和研究方向。