Emotion classification or emotion analysis is the natural language processing (NLP) technique that utilizes the technology to automatically classify human emotion based on the emotional tendency in their text. Emotion classification mainly focuses on analyzing subjective information such as views, opinions, emotions in the user-s text, which identifies the sentimental polarities and classifies the text correspondingly.
Traditional methods for emotion classification are time-consuming and error-prone due to their handcrafted feature extraction. Deep learning attains significant breakthroughs in natural language processing (NLP) tasks. Deep learning employs deep neural networks to extract the features from the text automatically and classifies the emotions of the text. Deep neural network-based emotion classification improves the performance of the learning model with a better classification rate.
• Emotion classification has grown as significant prominence in text-mining and natural language processing and forms an underlying part of affective computing.
• Automatic emotion classification boosts the advancement of the human-computer interface, concentrating on cognizant logical reactions toward various conditions.
• Deep learning models are highly applied, as they accurately and automatically classify emotions.
• Owing to the significant rise of deep learning techniques for emotion analysis application, it widely utilizes physiological, text, speech, video, facial expression, and multimodal features to perform effective emotion classification.
• Deep learning models for emotion classification yield high performance under a controlled environment.
• Improvement is needed to develop a real-time automated emotion analysis system using deep learning models for the dynamic scenario.