Artificial Intelligence and Communication Technologies
Multi-Label Emotion Classification Using Physically Explicated Dataset
Authors: Sowndarya Spoorthi B , Harshitha V, Nagasandesh N, K Sathya Pramod and Shashank N
Publishing Date: 09-03-2023
ISBN: 978-81-955020-5-9
Abstract
Multi-label emotions detection and classification in Facebook, Twitter, and other social media may be a demanding work because of general nature of the linguistics employed in these styles of platforms. Most of the previous studies mainly targeting single-label emotion detection, which detected only one emotion in a very given piece of text. But human expressions are multiplex, it should have multiple emotions with different semantics. So, during this study, we mainly target multi-label emotion classification which may classify possible different emotions in a very given text or data. Multi-label categorization has become the first solution among classification problems. Multi-label emotion categorizing may be a supervised learning that focuses on classifying multi-label emotions from a given data which contains a wide selection of implementations in Marketing, Electronic learning, education, and medical management, etc. to form the classification more efficient we use manually curated datasets labelled for basic eight emotion categories, these 8 basic emotions are based on Plutchik's model, which has the physiological purpose of each.
Keywords
Emotion classification, multi-label classification, NLP, Plutchik's wheel of Emotion.
Cite as
Sowndarya Spoorthi B , Harshitha V, Nagasandesh N, K Sathya Pramod and Shashank N, "Multi-Label Emotion Classification Using Physically Explicated Dataset", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2023, pp. 753-763. https://doi.org/10.52458/978-81-955020-5-9-71