File size: 2,400 Bytes
3f9e54b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # Urdu Multi-Modal Sentiment Analysis (UMSA)
[](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.
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## 📄 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)
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## 📊 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
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## 🧠 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.
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## 🧪 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
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## 📁 Repository Structure
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