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- W2898599105 abstract "In this chapter, we present different methods based on classical machine learning and modern Deep Learning based for multimodal sentiment analysis. To accomplish multimodal fusion for sentiment analysis we extract features from textual, audio and visual modality. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively. This chapter also proposes an extensive study on decision-level fusion." @default.
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- W2898599105 date "2018-01-01" @default.
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- W2898599105 title "Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis" @default.
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- W2898599105 doi "https://doi.org/10.1007/978-3-319-95020-4_7" @default.
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