A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Systematic Literature ReviewMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Emotion Detection in Text: a Review
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a…
MarketingWhy We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce
Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion detection and explanatory span identificat…
Automatic Sensor-free Affect Detection: A Systematic Literature Review
Emotions and other affective states play a pivotal role in cognition and, consequently, the learning process. It is well-established that computer-based learning environments (CBLEs) that can detect and adapt to students…
Systematic Literature ReviewMachine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques…
Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs
People express their opinions and emotions freely in social media posts and online reviews that contain valuable feedback for multiple stakeholders such as businesses and political campaigns. Manually extracting opinions…
Transfer Learning