youtube-travel-buzz-relevance-classifier

youtube-travel-buzz-relevance-classifier

🔹 Model Name

youtube-travel-buzz-relevance-classifier

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🔹 Model Description

A Korean NLP model for identifying travel-related YouTube comments as the first-stage filter in a travel demand signal extraction pipeline.

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🔹 Model Summary

This model is designed as the first-stage classifier in an NLP pipeline that transforms unstructured YouTube travel buzz into structured quantitative signals.

It classifies Korean YouTube comments into:

• Travel-related
• Non-travel-related

Only comments identified as travel-related are passed downstream for sentiment decomposition and demand signal analysis.

Unlike keyword-based filtering, this model focuses on contextual travel relevance, making it robust to noisy, colloquial, and informal YouTube comment language.

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🔹 Intended Use

Primary Use Case

• Filtering raw YouTube comments to extract semantically valid travel-related discourse
• Supporting:
  • Sentiment analysis
  • Buzz signal quantification
  • Downstream travel demand research

Out-of-Scope Use

• General-purpose topic classification
• Fine-grained intent detection (e.g., booking prediction)
• Real-time moderation or spam detection

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🔹 Training Data

• Type: Synthetic Korean YouTube comments generated using multiple LLMs
• Labels:
  • 1: Travel-related
  • 0: Non-travel-related
• Key Characteristics:
  • Informal language, slang, typos, emojis
  • Mixed sentence length and ambiguity
  • Designed to approximate real-world YouTube comment noise

Prompt design details and data generation strategy are documented in the associated GitHub repository.

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🔹 Model Architecture

• Base model: monologg/koelectra-small-discriminator
• Task: Binary sequence classification
• Tokenizer: KoELECTRA tokenizer
• Fine-tuning: Hugging Face Trainer API

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🔹 Performance (Indicative)

• Accuracy: ~95%
• F1-score: ~95%

Metrics are reported for reference only.

The primary objective of this model is robust filtering under noisy conditions, not leaderboard optimization.

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🔹 Limitations

• Trained on synthetic data; distribution shift may occur on unseen domains
• Subtle sarcasm or highly implicit references may be misclassified
• Not optimized for long-form comments or paragraph-level inputs

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🔹 Ethical Considerations

• No personal or sensitive data was used in training
• Model outputs should not be interpreted as indicators of individual intent

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🔹 Related Resources

• 📁 Full pipeline code and documentation: https://github.com/DalDream/youtube-travel-buzz-nlp-pipeline
• 🔗 Downstream sentiment classifier (travel-only): https://huggingface.co/DalDream/youtube-travel-buzz-sentiment-classifier

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🔹 Citation / Attribution

This model was developed as part of a YouTube Travel Buzz Signal Extraction NLP pipeline for research and portfolio demonstration purposes.

Author / Contributions

• [DalDream] – Project lead for model strategy, pipeline design, model validation, and final documentation.
• [GY Yu] – LLM-based synthetic data generation, dataset construction, model training, and fine-tuning.

Note: This model is the result of a collaborative team project. Responsibilities are listed to clarify individual contributions.