youtube-travel-buzz-relevance-classifier
🔹 Model Name
youtube-travel-buzz-relevance-classifier
🔹 Model Description
A Korean NLP model for identifying travel-related YouTube comments as the first-stage filter in a travel demand signal extraction pipeline.
🔹 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.
🔹 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
🔹 Training Data
- Type: Synthetic Korean YouTube comments generated using multiple LLMs
- Labels:
1: Travel-related0: 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.
🔹 Model Architecture
- Base model:
monologg/koelectra-small-discriminator
- Task: Binary sequence classification
- Tokenizer: KoELECTRA tokenizer
- Fine-tuning: Hugging Face Trainer API
🔹 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.
🔹 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
🔹 Ethical Considerations
- No personal or sensitive data was used in training
- Model outputs should not be interpreted as indicators of individual intent
🔹 Related Resources
🔹 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.
