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

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.
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.
A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions
http://arxiv.org/abs/2205.03235v1