# MELD.Raw: A Multimodal Sentiment Analysis Framework for English and Arabic Dialects

> MELD.Raw is a deep learning framework that integrates three modalities—text, audio, and facial video—to support sentiment and emotion recognition for English and Arabic dialects. It implements three distinct architectures and has been evaluated on multiple benchmark datasets.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-05T21:02:33.000Z
- 最近活动: 2026-04-05T21:25:18.799Z
- 热度: 141.6
- 关键词: multimodal, sentiment analysis, emotion recognition, Arabic NLP, transformer, cross-modal attention, CMU-MOSI, MELD
- 页面链接: https://www.zingnex.cn/en/forum/thread/meld-raw
- Canonical: https://www.zingnex.cn/forum/thread/meld-raw
- Markdown 来源: floors_fallback

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## MELD.Raw: A Multimodal Sentiment Analysis Framework for English and Arabic Dialects (Introduction)

MELD.Raw is a deep learning framework developed by Kareem Waly that integrates three modalities—text, audio, and facial video—to support sentiment and emotion recognition for English and Arabic dialects. The framework implements three complementary architectures and has been evaluated on CMU-MOSI, MELD, and a custom Arabic dataset. It not only provides high-performance English models but also reveals the challenges of low-resource Arabic multimodal research.

## Project Background and Research Motivation

Sentiment analysis is a key task in natural language processing, but text-only methods struggle to capture the full picture of human emotions. In daily communication, non-verbal cues like tone, speech rate, and facial expressions convey rich emotional information. Multimodal sentiment analysis addresses this by analyzing text, audio, and visual signals simultaneously. MELD.Raw focuses on supporting English and understudied Arabic dialects, aiming to explore effective multimodal fusion solutions.

## Three Architecture Designs

The project optimizes three architectures for different tasks and datasets:
1. **Enhanced Transformer Encoder (CMU-MOSI)**：Uses cross-modal attention mechanism. Text is processed with DeBERTa-v3-base, audio with Whisper-base, and video with ViT-base-patch16. It achieves 80.06% accuracy and 0.8012 F1 score on the CMU-MOSI test set.
2. **Dual-Task Projection Fusion Model (MELD)**：Handles 7-class emotion recognition and 3-class sentiment classification simultaneously. Modal features are mapped via linear projection layers then concatenated for fusion. Emotion classification accuracy is 62.87% and sentiment classification is 68.93%.
3. **Arabic Cross-Modal Transformer**：Designed for Arabic dialects. Uses 4-head attention, label smoothing, and class-balanced loss to handle small datasets. Text is processed with Arabic BERT, audio with enhanced MFCC, and video with OpenCV+PCA dimensionality reduction.

## Datasets and Experimental Results

The framework was tested on three datasets:
| Dataset | Source | Sample Count | Modalities | Language | Best Results |
|--------|------|--------|------|------|----------|
| CMU-MOSI | CMU MultiComp Lab | 2199 | Text/Audio/Video | English | 80.06% accuracy, F1:0.8012 |
| MELD | SenticNet Lab |13707 | Text/Audio/Video | English | Emotion:62.87%, Sentiment:68.93% |
| AMSAER | Custom |412 | Text/Audio/Video | Arabic Dialect |39.68% accuracy, F1:0.3766 |
The performance of Arabic experiments is low mainly due to the small dataset size (only 288 training samples), revealing the bottleneck of insufficient Arabic multimodal corpora.

## Key Findings and Research Contributions

**Key Findings**：
- Cross-modal Transformer outperforms simple feature concatenation (as shown in CMU-MOSI results);
- Dual-task learning (emotion + sentiment) is feasible and mutually beneficial;
- Arabic multimodal NLP faces severe data shortage, and audio/visual cues are crucial for resolving text ambiguity.
**Contributions**：Provides English-Arabic comparison benchmarks, validates the feasibility of dual-task learning, reveals challenges of low-resource languages, and offers complete reproducible code.

## Application Scenarios and Future Directions

**Application Scenarios**：Customer service quality monitoring (analyze dialogue text/tone/expressions), content moderation (identify negative emotions in videos), mental health screening (detect depression/anxiety signals), Arabic social media sentiment analysis.
**Future Directions**：Collect larger Arabic multimodal corpora, explore semi-supervised/self-supervised learning to utilize unlabeled data, study English-Arabic cross-language transfer, optimize model efficiency for resource-constrained environments.