Urdu Multi-Modal Sentiment Analysis (UMSA)
UMSA is a robust and extensible framework for multi-modal sentiment analysis and emotion detection, focused specifically on Urdu-language product review videos. It combines textual, audio, and visual modalities using a fusion-based approach and ensemble modeling. This repository contains the implementation code, dataset details, model weights, and evaluation results described in our thesis and journal publication.
📄 Journal Publication
S. S. Malik et al., "Multi-Modal Emotion Detection and Sentiment Analysis," in IEEE Access, vol. 13, pp. 59790-59810, 2025.
📖 Read Full Paper
📊 Overview
In the digital era, online review videos play a vital role in shaping public opinion and consumer decisions. UMSA addresses the challenge of extracting sentiment from such content, especially for low-resource languages like Urdu.
UMSA offers:
- A multi-modal Urdu dataset (USD)
- End-to-end extraction and annotation of text, audio, and visual modalities
- Early fusion and late ensembling techniques
- Support for transfer learning
- Benchmarking on text-only and multi-modal datasets
🧠 Key Features
Dataset (USD):
Urdu Sentiment Dataset consisting of annotated videos with synchronized modalitiesMulti-Modality Handling:
Textextracted from transcribed speechAudiopreprocessed for emotional signalsVisual Framescaptured and annotated from videos
Model Fusion + Ensembling:
Each modality is modeled individually and then combined via ensemble strategies for final prediction.Use Case Evaluation:
Real-world product reviews evaluated to test generalization.
🧪 Performance Summary
UMSA achieves >80% classification accuracy on the USD dataset using multi-modal integration. Validation on external datasets (USCv1, UrduTweets) showed expected drop in performance due to modality mismatch.
Dataset , Models and Code
Due to big volume of Dataset, the main detail of Datasets, Models and Code is available on : https://www.kaggle.com/datasets/shoaib837/urdu-sentiments-dataset-usd