Dataset
earnings_call_mono
A private collection of cleaned earnings-call transcript segments derived from a Motley Fool Kaggle release, split into 135,306 training rows tagged with ticker, exchange, date, and chunk text.
Data preview
Dataset card
The earnings_call_mono dataset by alwaysgood is a cleaned, deduplicated monolingual text corpus of earnings-call chunks derived from a Motley Fool Kaggle source of unclear license. It contains a single split of 135,306 rows stored as JSON and is published as a private repository.
What this dataset contains
Each row represents a single cleaned text chunk from a company earnings call, with boilerplate and operator text removed and a minimum word_count filter of 20 applied. Processing provenance is recorded in an accompanying processing_manifest.json. The corpus is explicitly marked private and tied to an upstream Kaggle dataset whose license is listed as unknown, so redistribution is restricted until licensing is clarified.
Structure and fields
The schema has six string columns: id, text, ticker, exchange, date, and section. The id field is a hash-style identifier, while ticker and exchange provide the listed company and venue (for example NASDAQ: BILI), and date captures the original call timestamp. The section column distinguishes chunk types such as prepared remarks versus Q&A, and the text column holds the cleaned spoken content used for downstream modeling.
How analysts use it
Buy-side and quantitative teams can use the aligned ticker, exchange, and date fields to join the text column with returns, fundamentals, or estimates for event-study and NLP sentiment work. The prepared-versus-other section split supports segmenting prepared remarks from analyst Q&A, which is useful when testing whether Q&A tone carries incremental signal. Because the license is unstated and the repo is private, usage is effectively limited to internal pilots until rights are confirmed.
Documentation & references 1
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