2019
AAAI
AAAI 2019
Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM
Abstract
Abstract In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.
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Conference Pioneer
— AAAI 2019
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— video feature extraction
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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, Robotics, Security & Privacy, Speech & Audio
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Application Areas > Data Augmentation
Deep Learning > Architectures > Neural Networks
Interdisciplinary > Social > Affective Computing
Natural Language Processing > Applications > Sentiment Analysis
Computer Vision > Analysis > Video Understanding
Deep Learning > Architectures > Recurrent Neural Networks
Keywords
dimensionality reduction
feature extraction
principal component analysis
sentiment analysis
video classification
video understanding
bidirectional lstm
emotion classification
facial expression recognition
video sentiment
bidirectional long short-term memory
bidirectional long short term memory
video feature extraction