Alternative data datasets
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A labeled corpus used to teach machine learning models how to detect sentiment and emotional cues in written language.
Numerical sentiment indicators derived from financial press coverage and social platforms to gauge mood toward publicly listed companies worldwide.
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Opinion-scoring system that evaluates conventional news outlets, blog content, and social networks to produce equity-oriented sentiment signals.
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Dataset capturing public mood and opinions shared on social platforms.
Dataset capturing public mood and opinions shared on social platforms.
Computed metrics include news-based sentiment scores and the degree to which individual companies track commodity futures performance.
Tone indicators extracted from onshore research publications originating in China.
Social listening across numerous languages producing opinion and topic indicators from global online conversations.
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Insights generated from text-based employee evaluations posted on OpenWork, a Japan-based careers platform.
Brand- and ticker-level consumer mood indicators extracted from publicly available social media posts.
Long-running series of news, social, and ESG sentiment measures distributed through LSEG channels.
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Analytical outputs capturing sentiment, emotional tone, intensity, and controversial elements found in online video material.
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NLP-derived sentiment ratings and behavioral indicators extracted from corporate transcripts.
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Hourly Hyperliquid market, positioning, funding, sentiment & macro on one grid
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Indicators of retail investor mood and posting activity are drawn directly from the StockTwits community.
TEJ has categorized and assigned scores to corporate announcements from listed firms along with media-reported news events.
Streaming market intelligence drawn from digital-asset news coverage and online sentiment channels.
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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.
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Multi-modal dataset: Bridging GAAP accounting and LLM-powered NLP for the S&P 50
Sentiment analysis in Chinese aggregates Weibo, other social platforms and mainland news sources, aimed at informing A-share equity approaches.
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Augment investment research using worldwide ESG news coverage.
An intelligent research assistant for searching across filings, transcripts, broker research and news, built to support competitive intelligence and thesis validation.
Text analytics that pulls sentiment signals and event markers from regulatory filings, earnings call transcripts and news articles.
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Quarterly Earnings Call Transcripts for 10 NASDAQ companies from 2016-2020
Real-time and historical election vote counts delivered alongside structured AP news archives.
A complete dataset with all Article V convention applications, 1789-2024.
Leverage a LLM to extract data from financial reporting from 10-k, 10-Q, news and events, etc
Indicative by mParticle is a self-service analytics platform designed for business users to understand the customer journey in order to drive conversion, increase engagement, and retain customers.
Lexicon based NLP solution analyses transcripts to identify consumer financial difficulty, potential vulnerability and forgetfulness.
Programmatic feed of capital markets reporting covering analyst views, options flow and early trading session headlines.
This dataset is a filtered version of https://huggingface.co/datasets/vincha77/filtered_yelp_restaurant_reviews
Recurring projections of Tesla output built by decoding vehicle identification numbers in NHTSA registration data.
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.
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Research-vendor language analytics offering sentiment indices and textual signals aimed at institutional investment strategies.