Investigating Context-Aware Sentiment Classification Using Machine Learning Algorithms

In today’s world knowing the opinion of people is a widely used research topic in natural language processing, and context-aware sentiment analysis has emerged as an important research area in recent years. This chapter conducts a survey of context-aware sentiment classification using machine learning algorithms. In this, first, provide an overview of the key concepts and challenges in context-aware sentiment analysis, and then review the existing literature on context-aware sentiments using machine learning algorithms. The chapter classifies the studies depending on the types of machine learning (ML)-based algorithms used, which are supervised, unsupervised, and deep neural network–based learning, and discusses the strengths and weaknesses of each approach. Furthermore, we identify the major challenges in context-aware sentiment analysis using machine learning techniques, such as data sparsity, context feature selection, and model interpretability, and discuss the potential solutions and future directions. We also provide a summary of the applications of context-aware sentiments using ML techniques in different domains, which are marketing, customer service, restaurants, politics, and health. This survey gives a detailed overview by considering present state-of-the-art context-aware sentiment classification using machine learning algorithms and highlights potential opportunities along with challenges in this field.