Alternative data datasets
Browse by provider, category, marketplace, access, delivery and license
A proprietary mood metric extracted from transcripts of US corporate earnings calls.
Quarterly Earnings Call Transcripts for 10 NASDAQ companies from 2016-2020
The awinml/earnings_calls_transcripts dataset on the Hugging Face Hub packages a small collection of earnings-call transcript segments formatted as chat-style messages for fine-tuning language models. It is distributed as a parquet file and is tagged for text modality with libraries including datasets, pandas, mlcroissant, and polars.
S&P 500 earnings-call transcripts in Parquet under MIT, tens of thousands of calls. Independently compiled and comparable to other public transcript corpora for sentiment and tone work.
A collection of earnings call transcripts covering S&P 500 firms and other U.S. large-cap companies across the years 2005 through 2025, intended for financial analysis, NLP modeling, and sentiment research.
View source information and access options.
View source information and access options.
Earnings Call Transcripts of META (formerly Facebook) 2018-2025
Archive of earnings call transcripts for more than 4,500 firms, beginning in 2005.
20 Years of Quarterly Earning Call Transcripts of Apple (2005-2025)
S&P 500 earnings-call transcripts preprocessed into optimized Parquet, tens of thousands of calls (MIT). Ready for sentiment, QA and summarization research on the large-cap earnings season.
Earnings call transcripts for S&P 500 and other large U.S. companies covering 2005 through 2025, useful for financial research, natural language processing, and sentiment analysis.
Earnings-call transcripts for S&P 500 companies, tens of thousands of calls in English with timestamps, in Parquet (MIT). Direct input for call-tone and question-and-answer sentiment analysis across the large-cap earnings cycle.
Verbatim written records of quarterly earnings calls along with supporting event information for publicly listed companies worldwide.
Top 50 Companies' Earnings Call Transcripts, scraped from AlphaSpread
An English-language NLP corpus of cleaned earnings call transcripts gathered from public investor-relations pages, sized between 10,000 and 100,000 documents for financial analysis and LLM work.
Transcripts of earnings calls for Taiwanese companies.
18,755 earnings call transcripts scraped from Motley Fool
View source information and access options.
Stock-symbol-linked customer feedback and earnings call texts are processed with natural language processing to derive sentiment indicators, helping assess ESG exposure and brand perception.
Genuine support call recordings and written logs designed to help develop conversational AI, large language models, and automated service tools.
An intelligent research assistant for searching across filings, transcripts, broker research and news, built to support competitive intelligence and thesis validation.
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.
A static tabular extension of the Bose345/sp500_earnings_transcripts collection covering the same 2005 to 2025 calendar of S&P 500 earnings events, where each row represents a single company-quarter call identified by a stable episode_id and bundles the full transcript along with related SEC press materials and pre-event inputs suitable for supervised learning or reinforcement-style experimentation.
Text analytics that pulls sentiment signals and event markers from regulatory filings, earnings call transcripts and news articles.
Lexicon based NLP solution analyses transcripts to identify consumer financial difficulty, potential vulnerability and forgetfulness.
Earnings call transcripts, news articles, and sector or industry company information designed to support AI and large language model workflows.
A curated collection of earnings call recordings broken into intelligent segments and paired with high-accuracy transcripts produced by Mistral's Voxtral model, intended for ASR benchmarking, transcription quality studies, and audio-to-text alignment research.
Earnings-call transcripts from an IBM research project, tens of thousands of records with finance tags, in CC0. A filings-adjacent corpus for call-tone and question-and-answer analysis.
Earnings Call LLM Insights 📚 Read the Full Story: For a deep dive into the methodology, the wildest moments we found, and key takeaways, check out the blog post:KnowTrend.ai: Auto-Grading Ten Years of Earnings Calls for Prescience and Delusion This dataset contains LLM-generated analysis of ~70,000+ earnings call transcripts. The analysis was performed using Kimi k2-0905-preview, focusing on extra
A large scale collection of structured video annotations covering transcripts, summaries, sentiment, safety signals, and content metadata.
S&P 500 earnings call episodes from 2005 to 2025 are released as static tabular rows, each identified by a company-quarter episode identifier and containing full transcripts along with SEC press materials, suited to supervised or reinforcement-style experiments.
Applies natural language processing to earnings call transcripts in order to surface KPI-related signals such as risk factors, strategic pivots, and pricing pressures.
NLP-derived sentiment ratings and behavioral indicators extracted from corporate transcripts.
Daily TikTok posts tagged with #booktok, including video transcripts, engagement metrics, and creator-level statistics.
Earnings Call Transcript Lite is a small public-records dataset of corporate earnings call transcripts paired with short reference summaries. It is distributed as a CSV and is published on the Hugging Face Hub under an unstated license.
A service that connects clients with practitioners worldwide, offering recorded interview archives and studies that draw on multiple specialists.
Enriched entity records and linking attributes that map corporations, individuals and locations together to power graph-style analysis.