Sequential Late Fusion Technique for Multi-modal Sentiment Analysis
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Date
2021-07-02Author
Banerjee, Debapriya
Lygerakis, Fotios
Makedon, Fillia
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Multi-modal sentiment analysis plays an important role for providing better interactive experiences to users. Each modality in
multi-modal data can provide different viewpoints or reveal unique
aspects of a user’s emotional state. In this work, we use text, audio
and visual modalities from MOSI dataset and we propose a novel fusion technique using a multi-head attention LSTM network. Finally,
we perform a classification task and evaluate its performance.