scholarly journals Cyberbullying Detection by Including Emotion Model using Stacking Ensemble Method

2021 ◽  
Vol 6 (5) ◽  
pp. 229-236
Author(s):  
Natasia Natasia ◽  
Sani Muhamad Isa
Keyword(s):  
Ensemble Method ◽  
Emotion Model ◽  
Cyberbullying Detection

Clustering ensemble method based on co-occurrence similarity

2011 ◽  
Vol 31 (2) ◽  
pp. 441-445 ◽  
Author(s):  
Guang LING ◽  
Ming-chun WANG ◽  
Jia-yi FENG
Keyword(s):  
Ensemble Method ◽  
Clustering Ensemble

Cyberbullying Detection, Based on the FastText and Word Similarity Schemes

2020 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Kun Wang ◽  
Yanpeng Cui ◽  
Jianwei Hu ◽  
Yu Zhang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Word Similarity ◽  
Cyberbullying Detection

Cyberbullying detection: Utilizing social media features

2021 ◽  
Vol 179 ◽  
pp. 115001
Author(s):  
Alican Bozyiğit ◽  
Semih Utku ◽  
Efendi Nasibov
Keyword(s):  
Social Media ◽  
Cyberbullying Detection

scholarly journals Multiclass emotion prediction using heart rate and virtual reality stimuli

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Aaron Frederick Bulagang ◽  
James Mountstephens ◽  
Jason Teo
Keyword(s):  
Heart Rate ◽  
Virtual Reality ◽  
Nearest Neighbor ◽  
Subject Classification ◽  
Support Vector ◽  
Display Device ◽  
K Nearest Neighbor ◽  
Potential Applications ◽  
Health Rehabilitation ◽  
Emotion Model

Abstract Background Emotion prediction is a method that recognizes the human emotion derived from the subject’s psychological data. The problem in question is the limited use of heart rate (HR) as the prediction feature through the use of common classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) in emotion prediction. This paper aims to investigate whether HR signals can be utilized to classify four-class emotions using the emotion model from Russell’s in a virtual reality (VR) environment using machine learning. Method An experiment was conducted using the Empatica E4 wristband to acquire the participant’s HR, a VR headset as the display device for participants to view the 360° emotional videos, and the Empatica E4 real-time application was used during the experiment to extract and process the participant's recorded heart rate. Findings For intra-subject classification, all three classifiers SVM, KNN, and RF achieved 100% as the highest accuracy while inter-subject classification achieved 46.7% for SVM, 42.9% for KNN and 43.3% for RF. Conclusion The results demonstrate the potential of SVM, KNN and RF classifiers to classify HR as a feature to be used in emotion prediction in four distinct emotion classes in a virtual reality environment. The potential applications include interactive gaming, affective entertainment, and VR health rehabilitation.

scholarly journals When the timeline meets the pipeline: A survey on automated cyberbullying detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fatma Elsafoury ◽  
Stamos Katsigiannis ◽  
Zeeshan Pervez ◽  
Naeem Ramzan
Keyword(s):  
Cyberbullying Detection
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