Twitter/X Sentiment Data
Buy and sell twitter/x sentiment data data. Real-time sentiment, trending topics, and engagement patterns — the public discourse data.
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Find Me This Data →Overview
What Is Twitter/X Sentiment Data?
Twitter/X sentiment data captures real-time emotional and opinion signals from public discourse on the X platform. This includes sentiment analysis of tweets, trending topics, engagement patterns, and the collective mood of users discussing stocks, events, brands, and global issues. The data reflects how millions of users—particularly younger demographics aged 18-34—express views on finance, politics, and culture through short-form posts. Sentiment data has proven valuable for predicting market movements, as demonstrated by research showing X posts can meaningfully impact stock returns through machine learning analysis.
Market Data
498 million tweets
Finance & Stocks Tweets (90 Days, 2023)
Source: X Business
65% aged 18-34
Young Users in Finance Discussions
Source: X Business
1/3 of total user reports
Abuse/Harassment Reports (H1 2024)
Source: Electroiq
23.21%
US Traffic Share (Oct 2023–Mar 2024)
Source: Electroiq
Who Uses This Data
What AI models do with it.do with it.
Financial Services & Trading Firms
Use sentiment analysis to predict stock returns and market movements. Research demonstrates AI models can analyze tweet sentiment to forecast equity performance.
Marketing & Brand Strategy Teams
Monitor real-time engagement metrics, trending topics, and audience sentiment to refine content strategy and understand brand perception.
Political & Policy Research
Track public opinion shifts on legislation, elections, and political figures through aggregated sentiment patterns and discussion trends.
Risk & Compliance Monitoring
Identify emerging issues, hate speech, harassment patterns, and reputational threats through sentiment classification and abuse detection.
What Can You Earn?
What it's worth.worth.
Real-Time Sentiment Feeds
Varies
Live tweet sentiment scores, trending hashtags, and engagement metrics—typically subscription-based with volume/speed tiers.
Historical Sentiment Datasets
Varies
Bulk archives of classified tweets with sentiment labels for machine learning training and backtesting.
Custom Sentiment Analysis
Varies
Tailored sentiment models for specific keywords, accounts, or industries—pricing depends on scope and refresh frequency.
What Buyers Expect
What makes it valuable.valuable.
Accuracy & Classification Quality
Sentiment labels must be precise (positive, negative, neutral) with high inter-rater agreement; machine learning models should achieve 85%+ accuracy on validation sets.
Real-Time or Near-Real-Time Delivery
For active trading use cases, sentiment data must be available within minutes of tweet posting; batch feeds require reliable daily/hourly schedules.
Coverage & Completeness
Datasets must include representative sampling across geographies, user demographics, and topics; bias toward major accounts or US English must be disclosed.
Compliance & Content Moderation Transparency
Data providers must clearly document how hateful content, spam, and abuse reports are handled; filtering and exclusion rules must be transparent.
Companies Active Here
Who's buying.buying.
Integrate X sentiment feeds into algorithmic models to forecast equity movements and identify alpha signals.
Publish sentiment indices and market commentary based on real-time X discourse trends.
Track brand sentiment, competitor mentions, and campaign engagement to optimize social strategy.
Monitor sentiment on elections, policies, and public figures to supplement traditional surveys.
FAQ
Common questions.questions.
How can X/Twitter sentiment data impact stock prices?
Research shows that AI models analyzing X sentiment can correlate tweet tone and volume with stock returns. High-volume discussions on finance topics (498 million tweets in 90 days in 2023) can signal market sentiment shifts that precede or accompany price moves, particularly in retail-driven stocks.
What types of sentiment analysis models work best with X data?
Studies demonstrate that Support Vector Machines, Term Frequency-Inverse Document Frequency (TF-IDF), and neural networks can classify tweet sentiment effectively. Models should handle non-English text, emoticons, and informal language to capture the full breadth of X discourse.
What quality issues should I be aware of when buying X sentiment data?
Key concerns include spam accounts (historically 5% of daily active users), abuse and harassment reports (one-third of user reports in H1 2024), and hateful content. Verify that datasets exclude spam, are clearly labeled for content moderation, and disclose geographic/demographic biases.
Is X sentiment data valuable for non-financial use cases?
Yes. Marketing teams use it for brand monitoring and campaign optimization, political researchers track public opinion on elections and policy, and compliance teams identify emerging reputation risks. The platform's 300+ million monthly active users generate signals across finance, politics, entertainment, and more.
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