Nikelroid/multimodal-sentiment-classification
A multimodal deep learning framework that fuses visual features from EfficientNet and textual features from BERT to classify sentiment in image-text conversations using the MSCTD dataset.
This project helps social media analysts and brand managers understand public opinion by automatically classifying sentiment in image-text conversations. It takes social media posts that combine images and text, processes both the visual cues (like faces) and the written content, and outputs a classification of the sentiment as positive, negative, or neutral. This helps anyone needing to quickly gauge the emotional tone of online discussions.
Use this if you need to analyze the emotional tone of social media posts or online dialogues where both images and text are important for understanding the full context.
Not ideal if your sentiment analysis only involves text, or if you need to analyze sentiment in data types other than image-text pairs.
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Last pushed
Nov 21, 2025
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