Emotion Detection of Textual Data: An Interdisciplinary Survey

Emotion is a primary aspect of communication and can be expressed in many modalities. Text-Based Emotion Detection (TBED), one of the fastest growing branches of Natural Language Processing (NLP), is the process of classifying syntactic or semantic units of a corpus into a given set of emotion classes proposed by a psychological model. Automatic TBED mechanisms use machine learning approaches to create computational platforms automating the process of extracting emotions. TBED has a wide variety of applications in the area of artificial intelligence: Semantic analysis of documents and public messages related to terrorist attacks (to mitigate risks), automated analysis of historical corpora, and study of product reviews (to assess customer satisfaction). This work reviews the current literature of TBED and the psychological models associated with them.

Automated summary

Text-Based Emotion Detection is a rapidly growing field in Natural Language Processing that automates the extraction of emotions using machine learning. It has a wide range of applications in artificial intelligence, such as semantic analysis, historical corpus analysis, and product review studies.