Upload README.md

README.md

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+# Urdu Multi-Modal Sentiment Analysis (UMSA)
+
+[![IEEE Publication](https://img.shields.io/badge/Published%20in-IEEE%20Access-blue)](https://doi.org/10.1109/ACCESS.2025.3552475)
+
+**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](https://doi.org/10.1109/ACCESS.2025.3552475)
+
+---
+
+## 📊 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
+