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A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email
8 references·0 citing·47 second hop
A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email (2020)A machine learning-based investigation ut…On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning (2019)On identification of driving-…GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition (2019)GCB-Net: Graph Convolutional …Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning (2018)Multi-Modal Emotion recogniti…Profiling Players Using Real-World Datasets: Clustering the Data and Correlating the Results with the Big-Five Personality Traits (2017)Profiling Players Using Real-…A Large-Scale Test of the Goldilocks Hypothesis (2017)A Large-Scale Test of the Gol…Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis (2016)Measuring Organizational Legi…Creatures of the semiosphere: A problematic third party in the ‘humans plus technology’ cognitive architecture of the future global superintelligence (2016)Creatures of the semiosphere:…A Combined Rule-Based & Machine Learning Audio-Visual Emotion Recognition Approach (2016)A Combined Rule-Based & Machi…Three dimensions of emotion. 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(2004)Explorations in Personality (2007)An artificial intelligence-based framework for data-driven categorization of computer scientists: a case study of world’s Top 10 computing departments (2022)Deploying a robotic positive psychology coach to improve college students’ psychological well-being (2022)Serious Games and AI: Challenges and Opportunities for Computational Social Science (2023)IEMOCAP: interactive emotional dyadic motion capture database (2008)A Multimodal Database for Affect Recognition and Implicit Tagging (2011)Speech emotion recognition using deep neural network and extreme learning machine (2014)EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach (2019)Attention Driven Fusion for Multi-Modal Emotion Recognition (2020)Multi-Modal Attention for Speech Emotion Recognition (2020)The social readjustment rating scale (1967)Measuring emotion: The self-assessment manikin and the semantic differential (1994)Emotional state classification from EEG data using machine learning approach (2013)An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes (2020)Multimodal Features for Detection of Driver Stress and Fatigue: Review (2020)Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals (2023)Differential entropy feature for EEG-based emotion classification (2013)Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015)EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2018)SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition (2020)EEG-Based Emotion Recognition Using Spatial-Temporal Graph Convolutional LSTM With Attention Mechanism (2022)Emotion recognition in EEG signals using deep learning methods: A review (2023)
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References

  • 2017A Large-Scale Test of the Goldilocks Hypothesis885 cited
  • 2019GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition396 cited
  • 2016Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis257 cited
  • 2019On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning231 cited
  • 2018Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning131 cited
  • 2016A Combined Rule-Based & Machine Learning Audio-Visual Emotion Recognition Approach91 cited
  • 2017Profiling Players Using Real-World Datasets: Clustering the Data and Correlating the Results with the Big-Five Personality Traits81 cited
  • 2016Creatures of the semiosphere: A problematic third party in the ‘humans plus technology’ cognitive architecture of the future global superintelligence20 cited

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