Human-Computer Interaction (HCI) encompasses the study of how humans interact with technology, including computer software, hardware, mobile devices, websites, and other digital interfaces. HCI traces back to the early stages when human-computer interactions were limited to command-line interfaces, but with the advancement in the digital world, HCI has become a significant area of research and development. As we journey towards a more emotionally intelligent technological era, emotion recognition in HCI stands as a critical enabler of richer, more meaningful human-computer interactions. This review paper aims to shed light on the progress made, challenges faced, and potential for growth in this fascinating domain. Additionally, it aims to analyze the various methodologies used to implement human-computer interaction using facial features and voice. The paper reviews various pre-existing systems that implement sentiment analysis on multiple datasets, their technology, the output, the accuracy they achieved, and their conclusion. Most of the systems reviewed here were implemented using Deep Learning algorithms like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and various other concepts like Feature Extraction, Data Preprocessing, etc.

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Facial Expression Recognition Convolutional Neural Network Preprocessing. Statistical Approach Electrooculography etc