Review of Psychometric Data Analysis for Healthcare Based on Emotional Intelligence
Main Article Content
Article Sidebar
Abstract
Purpose: The main purpose of the paper is to review the various existing systems for emotion analysis in audio, video and text using machine learning algorithms.
Methodology: This review is based on the data collected from more than 10 papers which describes how emotion analysis is performed in audio, video and text using machine learning algorithms. The findings and results of each paper are described in this paper of review.
Findings: The technological advancements in the current world and also due to the Covid-19 pandemic there is high competition among people resulting in locked-in syndrome, stress, and various other psychological problems like bipolar disorders, schizophrenia severe depression, So, the need for the proper psychometric analyser is tremendously increasing to analyse the emotions of people, so that a person’s emotion can be found and necessary actions can be taken according to their health condition. These data prove that the emotion analysers could help people to overcome their issues if there is a proper end to end communication between a psychologist and the affecter person through a web application or mobile application. Though these systems are found with some drawbacks, the accuracy of some systems is high.
Originality: From The data collected from different papers and the observation on these papers shows us the need for a proper website to help these people. So, the main objective of our review on these papers is to develop a project with the main objectives of as, (1) To identify the emotion of a person in the video, audio and text using machine learning models. (2) The proposed system makes it easy for the psychologist to identify the patient’s emotions in online consultations. (3) While talking in a video call the emotion of a person can be identified using his/her facial expressions. In an audio phone call, the emotion is recognized using the tone of the person. Text chat emotions can also be identified using the words and their context.
How to Cite
Article Details
Psychometric analyzer, facial expression, audio emotion, text emotion.
[2] Abdullah TalhaKabakus, “PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks”, IEEE Access (Volume 8), 29 July 2020.https://doi.org /10.1109/ACCESS.2020.3012703
[3] KunZheng et al, “Recognition of Teachers’ Facial Expression Intensity Based on Convolutional Neural Network and Attention Mechanism”,21 December 2020. https://doi.org/10.1109/ACCESS.2020.3046225
[4] Xiangjian Chen et al,“A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER”, IEEE Access (Volume 8), 20 December 2019.https://doi.org/10.1109/ ACCESS.2019.2960769
[5] Jorge oliveiraandIsabelpraca, “On the Usage of Pre-Trained Speech Recognition Deep Layers to Detect Emotions”, IEEE Access(Volume 9), 9 January 2021. https://doi.org/10.1109/ACCESS.2021.3051083
[6] Ting-Wei Sun, “End-to-End Speech Emotion Recognition With Gender Information”, IEEE Access (Volume 8), 28 August 2020.https://doi.org/10.1109/ACCESS.2020.3017462
[7] Ruhul Amin Khalil1 et al, Tariqullah 1, Mohammad HaseebZafar 3, and ThamerAlhussain4, “Speech Emotion Recognition Using Deep Learning Techniques: A Review”, IEEE Access (Volume 7), 4 September 2019.https://doi.org/10.1109/ACCESS. 2019.2936124
[8] Lee SYM et al, “A text-driven rule-based system for emotion cause detectionAssociation for Computational Linguistics, Stroudsburg, pp 45–53.https://aclanthology.org/W10-0206
[9] Herzig J et al, “Emotion detection from text via ensemble classification using word embeddingsICTIR’17. ACM, New York, pp 269–272.https://dl.acm.org/doi/ 10.1145/3121050.3121093