PakDramaEcho: A DistilBERT-Labeled Urdu Sentiment Dataset

📊 PakDramaEcho Dataset Description & Research Interpretation

🧠 Research Hypothesis

This study assumes that sentiment expressed in YouTube comments on Pakistani drama content can be effectively modeled using transformer-based NLP (DistilBERT). It further hypothesizes that these sentiments reflect meaningful emotional patterns linked to storylines, characters, and dramatic events in Urdu dramas.

Specifically:

* Informal Urdu/Roman Urdu still contains strong sentiment signals despite noise
* DistilBERT can perform reliable sentiment labeling in low-resource settings
* Viewer comments reflect real audience emotional response to dramas

📦 Data Overview

PakDramaEcho is a sentiment analysis dataset created from YouTube comments on Pakistani drama videos.

Source: YouTube drama comment sections
Language: Urdu, Roman Urdu, mixed English-Urdu
Domain: Pakistani TV dramas
Task: Sentiment classification (Positive / Neutral / Negative)
Labeling 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