PakDramaEcho: A DistilBERT-Labeled Urdu Sentiment Dataset

Method

DistilBERT-based automatic annotation Format: CSV file 🧹 Data Collection & Processing Collection

• Scraped publicly available YouTube comments from drama-related videos
• Only public comments were included Preprocessing
• Removed URLs, emojis, and special characters
• Cleaned repeated/noisy text
• Normalized Urdu text
• Filtered empty/irrelevant comments
• Optional deduplication applied Labeling
• Sentiment labels generated using DistilBERT classifier
• Classes: Positive, Neutral, Negative 📈 Key Insights 1. Strong Emotional Engagement Most comments are emotionally expressive, especially positive ones, showing strong audience connection with drama content. 2. Noisy & Informal Language Includes Roman Urdu, spelling variations, and mixed-language text, reflecting real-world social media challenges. 3. Sentiment Imbalance Positive sentiment dominates, likely due to fan engagement and selective commenting behavior. 4. Context-Dependent Emotion Sentiment depends on characters, emotional scenes, and plot twists rather than isolated words. 🔍 Interpretation This dataset should be treated as a real-world noisy corpus rather than a clean benchmark. It represents:
• Audience perception, not objective truth
• Emotional response to entertainment media
• A resource for low-resource Urdu NLP research ⚠️ Since labels are generated using DistilBERT, there may be classification noise and model bias. 🚀 Potential Use Cases
• Urdu sentiment classification benchmarking
• Transformer model training (BERT, RoBERTa, DistilBERT)
• Low-resource NLP and domain adaptation
• Social media opinion mining
• Audience behavior analysis ⚠️ Limitations
• Auto-generated labels (not human verified)
• Domain-specific (Pakistani dramas only)
• Noisy informal text
• Possible sentiment imbalance