Sequential Late Fusion Technique for Multi-modal Sentiment Analysis

Debapriya Banerjee; Fotios Lygerakis; Fillia Makedon

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arXiv · 2106.11473

Sequential Late Fusion Technique for Multi-modal Sentiment Analysis

Debapriya Banerjee·Fotios Lygerakis·Fillia Makedon

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Abstract

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.

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Debapriya Banerjee, Fotios Lygerakis, Fillia Makedon. 2021-06-22. Sequential Late Fusion Technique for Multi-modal Sentiment Analysis. https://arxiv.org/abs/2106.11473

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