How does Multimodal AI improve sentiment analysis for social media monitoring?

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Dealing with sarcasm is the "Holy Grail" of sentiment analysis, and multimodality is the only way to crack it. We recently implemented a system using a late-fusion transformer architecture that takes inputs from an audio-emotion encoder and a video-frame facial expression recognizer. The key is to look for "affective dissonance"—where the text is positive (e.g., "I love this product") but the audio pitch indicates frustration or the visual shows a micro-expression of disgust. By weighting these modalities, we’ve seen a 25% increase in accuracy for brand sentiment reports compared to our old NLP-only approach.

   Answered 2024-09-08 by Susan Martinez

Are you using a pre-trained multimodal model for this, or are you trying to train a custom head on top of something like CLIP or ImageBind?

   Answered 2024-09-10 by David Johnson

• We are currently experimenting with Meta's ImageBind because it supports six different modalities. However, we are finding it difficult to find high-quality labeled datasets for "sarcastic" video content to fine-tune the model. Do you have any recommendations for open-source datasets that focus on multi-channel emotional contradictions?

     Commented 2024-09-12 by Richard Moore

Cross-modal grounding is vital here. You need the model to understand that the "audio energy" and "facial valence" are just as important as the word count.

   Answered 2024-09-14 by Linda Davis

• Spot on, Linda. If the model isn't grounded across all three channels, it will always default to the text, which is usually the most misleading part of sarcasm.

     Commented 2024-09-15 by Ashley Thompson