soujanyaporia/multimodal-sentiment-analysis
Attention-based multimodal fusion for sentiment analysis
This project helps researchers and data scientists understand the emotional tone behind user-generated video content. By analyzing text, audio, and visual cues from video utterances, it identifies overall sentiment (like positive, negative, or neutral). You feed it raw video features, and it outputs sentiment classifications for each video.
366 stars. No commits in the last 6 months.
Use this if you need to classify sentiment from user-generated videos by combining information from spoken words, vocal tone, and visual expressions.
Not ideal if you need to analyze sentiment from text-only data or require real-time processing, as this is a research-focused implementation.
Stars
366
Forks
74
Language
Python
License
MIT
Category
Last pushed
Apr 08, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/soujanyaporia/multimodal-sentiment-analysis"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Cyberbolt/Cemotion
A Chinese NLP library based on BERT for sentiment analysis and general-purpose Chinese word...
juliusberner/emotion_transformer
Contextual Emotion Detection in Text (DoubleDistilBert Model)
ahmedbesbes/multi-label-sentiment-classifier
How to build a multi-label sentiment classifiers with Tez and PyTorch
faezesarlakifar/text-emotion-recognition
Persian text emotion recognition by fine tuning the XLM-RoBERTa Model + Bidirectional GRU layer.
firojalam/multimodal_social_media
multimodal social media content (text, image) classification