2022
IJCNLP
IJCNLP 2022
Emotional Intensity Estimation based on Writer’s Personality
Abstract
AbstractWe propose a method for personalized emotional intensity estimation based on a writer’s personality test for Japanese SNS posts. Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. We personalize the emotion analysis using not only the text but also the writer’s personality information. Experimental results show that personality information improves the performance of emotional intensity estimation. Furthermore, a hybrid model combining the existing personalized method with ours achieved state-of-the-art performance.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Human-AI Interaction
Artificial Intelligence > Core AI > Multimodal Learning
Machine Learning > Core Methods > Regression
Machine Learning > Application Areas > Domain Adaptation
Interdisciplinary > Social > Affective Computing
Natural Language Processing > Applications > Sentiment Analysis