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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 modalities
Multi-Modality Handling:
- Text extracted from transcribed speech
- Audio preprocessed for emotional signals
- Visual Frames captured 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

📁 Repository Structure

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