| --- |
| license: other |
| license_name: research-and-educational-use-only |
| license_link: LICENSE |
| --- |
| |
| language: |
| - en |
|
|
| tags: |
| - finance |
| - earnings-calls |
| - transcripts |
| - nlp |
| - llm |
| - rag |
| - financial-analysis |
|
|
| license: other |
| pretty_name: Earnings Call Transcripts |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Earnings Call Transcripts Dataset |
| |
| A cleaned financial NLP dataset containing earnings call transcripts collected from publicly available earnings call pages. |
| |
| ## Dataset Overview |
| |
| This dataset contains: |
| |
| - Company earnings call transcripts |
| - Ticker symbols |
| - Earnings quarters |
| - Earnings years |
| - Call dates |
| - Cleaned transcript text |
| - Source URLs |
| - Metadata timestamps |
| |
| The dataset was processed through a custom ETL pipeline involving: |
| |
| 1. URL discovery |
| 2. HTML scraping |
| 3. Transcript extraction |
| 4. Data validation |
| 5. Cleaning and normalization |
| 6. SQLite persistence |
| 7. Pandas validation and EDA |
| 8. Parquet export |
| |
| ## Dataset Format |
| |
| The dataset is distributed as: |
| |
| ```text |
| transcripts_clean.parquet |
| |
| Schema |
| Column Description |
| ticker Stock ticker |
| company Company name |
| quarter Earnings quarter |
| earnings_year Earnings year |
| call_date Earnings call date |
| title Earnings call title |
| transcript Cleaned transcript |
| source_url Original source URL |
| scraped_at Scraping timestamp |
| Use Cases |
| Financial NLP |
| Retrieval-Augmented Generation (RAG) |
| LLM fine-tuning |
| Earnings sentiment analysis |
| Financial Q&A systems |
| Market intelligence research |
| Temporal financial analysis |
| Disclaimer |
| |
| This dataset was created for research and educational purposes only. |
| |
| The original transcript content originates from publicly accessible earnings call transcript pages. |
| |
| Users are responsible for ensuring compliance with all applicable terms, copyrights, and data usage policies associated with the original content providers. |
