HiBorn4/TensorFusion_Network_for_Multimodal_sentiment_analysis
This repository implements the Tensor Fusion Network (TFN) for multimodal sentiment analysis using the CMU-MOSI dataset. TFN integrates language, visual, and acoustic modalities to predict sentiment intensity, enhancing sentiment prediction accuracy by modeling unimodal, bimodal, and trimodal interactions.
This project helps researchers and data scientists analyze sentiment from video opinions by integrating spoken language, facial expressions, and acoustic cues. It takes video data with these three modalities and outputs a sentiment intensity score or classification (e.g., positive, negative). This is ideal for those studying emotional responses or public opinion expressed in video content.
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Use this if you need highly accurate sentiment analysis for video data, especially when subtle emotional nuances across speech, visuals, and sound are important.
Not ideal if your data is purely text-based or if you don't have access to detailed visual and acoustic features from video content.
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May 21, 2024
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