Papers › Gated Mechanism for Attention Based Multimodal Sentiment Analysis
Gated Mechanism for Attention Based Multimodal Sentiment Analysis
Ayush Kumar, Jithendra Vepa
Multimodal sentiment analysis has recently gained popularity because of its relevance to social media posts, customer service calls and video blogs. In this paper, we address three aspects of multimodal sentiment analysis; 1. Cross modal interaction learning, i.e. how multiple modalities contribute to the sentiment, 2. Learning long-term dependencies in multimodal interactions and 3. Fusion of unimodal and cross modal cues. Out of these three, we find that learning cross modal interactions is beneficial for this problem. We perform experiments on two benchmark datasets, CMU Multimodal Opinion level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Our approach on both these tasks yields accuracies of 83.9% and 81.1% respectively, which is 1.6% and 1.34% absolute improvement over current state-of-the-art.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Sentiment Analysis | CMU-MOSEI | Proposed: B2 + B4 w/ multimodal fusion | Accuracy | 81.14 | #9 of 15 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | MOSI | Proposed: B2 + B4 w/ multimodal fusion | Accuracy | 83.91% | #5 of 11 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | MOSI | Proposed: B2 + B4 w/ multimodal fusion | F1 score | 81.17 | #5 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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