Download PDFOpen PDF in browserVideo-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTMEasyChair Preprint 6572 pages•Date: November 29, 2018AbstractIn 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. Keyphrases: Sentiment Analysis, cmu mosi dataset, facial expression recognition, feature extraction, machine learning, moud dataset, video based sentiment analysis, video sentiment analysis
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