A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

• URL: http://arxiv.org/abs/2205.03235v1
• Date: Tue, 26 Apr 2022 15:20:00 GMT
• Title: A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions
• Authors: Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora, Ilias Pappas
• Abstract summary: The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in text-based emotion detection. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.
• Score: 4.257210316104905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.

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