| # Urdu Multi-Modal Sentiment Analysis (UMSA) |
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| [](https://doi.org/10.1109/ACCESS.2025.3552475) |
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| **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. |
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| ## 📄 Journal Publication |
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| > **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) |
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| ## 📊 Overview |
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| 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. |
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| 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 |
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| ## 🧠 Key Features |
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| - **Dataset (USD):** |
| Urdu Sentiment Dataset consisting of annotated videos with synchronized modalities |
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| - **Multi-Modality Handling:** |
| - `Text` extracted from transcribed speech |
| - `Audio` preprocessed for emotional signals |
| - `Visual Frames` captured and annotated from videos |
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| - **Model Fusion + Ensembling:** |
| Each modality is modeled individually and then combined via ensemble strategies for final prediction. |
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| - **Use Case Evaluation:** |
| Real-world product reviews evaluated to test generalization. |
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| ## 🧪 Performance Summary |
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| 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. |
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| ## Dataset , Models and Code |
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| 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 |
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| ## 📁 Repository Structure |
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