학술논문

Experimental Comparison of Machine Learning Techniques for Analysing the Facial Expression
Document Type
Conference
Source
2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021 9th International Conference on. :1-5 Sep, 2021
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Uncertainty
Webcams
Semantics
Neural networks
XML
Predictive models
Planning
Machine Learning
Natural Language Processing(NLP)
Sentimental Analysis and Neural Network
Language
Abstract
Emoticons are miniature pictures that are customarily used in internet community Communications in the 21st century. The fusion of textual and imagery contained in the same message develops today's modern way of conversation. In spite of being universally utilized in online media, Emoticons basic interpretation has received very little observation from a “Natural Language Processing” point of view. In this paper, we investigate the relation between facial expressions and emoticons, studying the novel task of predicting which emojis are evoked by the user's facial expressions. We experimented with variants of word embedding techniques, and train various models based on MNBs and LSTMs in this task respectively. The experimental results show that our model can predict reasonable emoticons from emotions.