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Determining the Sentiments of Texts with News API
Benefits
Determining the Sentiments of Texts with News API Features
Text sentiment analysis uses natural language processing (NLP) and machine learning to automatically determine the emotional tone of written content. Our system analyzes word choice, context, phrases, and linguistic patterns to classify text as positive, negative, or neutral with a confidence score.
Learn more about API Use cases
Discover the possibilities
Our solutions are designed to help you achieve your goals. Whether you are a small business or a large corporation, we have the right solution for you.
MLTraining-data augmentation
Bootstrap labeled datasets using APITube's sentiment scores; refine with human review on a smaller sample.
TradingSignal pipelines
Plug per-article sentiment polarity and intensity into quant signal pipelines and backtests.
ProductIn-app sentiment overlays
Surface sentiment badges next to news articles in news-reader, finance, or analytics apps.
ResearchTopic-sentiment dashboards
Build dashboards showing sentiment by topic, geography, and publisher tier for any custom domain.
OperationsInternal-tool enrichment
Enrich internal news feeds (Slack, Teams, intranet) with sentiment labels so analysts triage faster.
ValidationCross-source consensus scoring
Compare sentiment across multiple sources on the same event to detect outliers or framing differences.
Features
Don’t waste time on complex features
Complex features made simple. Our API provides a simple way to access news articles from around the world. We provide a simple, consistent, and easy-to-use API to access news articles from thousands of sources.
News API
- Export data in many formats
- Industry Monitoring
- Brand Monitoring
- Market Intelligence
- Risk Management
- Competitive Intelligence
- Media Monitoring
- Sentiment Analysis
- Trend Analysis
- Story Grouping
- Forecasting social trends
- Multi-language Support
- Audience Engagement
- Geographical Analysis
- Real-time Breaking News
- Historical Data Access
- Custom News Feeds
- News Aggregation
- Content Filtering
- Over 50 integrations
Extract Additional Data
- Industries
- Locations
- Persons
- Organizations
- Brands
- Events
- Disasters
- Diseases
- Links
- Media
- Images & Videos
- Hashtags
- Authors
- Source
- Duplicate Detection
- Publisher Rank
- Article Sentiment
- Readability Score
- Language Detection
Analysis
- Sentiment Analysis
- Analysis of public opinion
- Categorization
- Financial Analysis
- Trend Analysis
- Story Grouping
- Content Summarization
- Entity Recognition
- Keyword Extraction
- Topic Modeling
- Event Detection
- Named Entity Recognition
- Text Classification
- Controversy Detection
- Trust Score Analysis
- Engagement Metrics
- Source Bias Detection
- Quality Ranking
- Spam Detection
- Emotion Detection
Advanced Searching
- Search by Location
- Search by Date Range
- Search by Source
- Search by Category
- Search by Industry
- Search by Sentiment
- Search by Story
- Search by Publisher Rank
- Search by Language
- Search by Entity
- Search by Keywords
- Boolean Search
- Proximity Search
- Faceted Search
- Range Queries
- Search by Author
- Search by Media Type
- Search by Breaking News
- Search by Read Time
Interactive Demo
Pick a scenario and see real API responses — filtered, structured, and ready to use. No credentials needed.
News API Scenarios
Ready to build with News API?
Discover recipes and code examples for common news data workflows.
API Response
Loading...More than 500,000+ sources
APITube is trusted by teams around the world to help them build and deliver amazing digital experiences faster than ever before.
- Last updated
- 7s ago
- Total sources
- 512.645k
- Requests yesterday
- 28.572
- Articles added yesterday
- 127.559k
- Total articles
- 3.82b
Frequently asked questions
- Text sentiment analysis uses natural language processing (NLP) and machine learning to automatically determine the emotional tone of written content. Our system analyzes word choice, context, phrases, and linguistic patterns to classify text as positive, negative, or neutral with a confidence score.
- Polarity indicates the direction of sentiment (positive, negative, neutral), while the sentiment score (0-1) measures intensity. A score of 0.9 with positive polarity indicates strongly positive content, while 0.5 suggests mildly positive or mixed sentiment.
- Our models achieve 85%+ accuracy across general news categories, with specialized training for business, politics, technology, and entertainment content. Accuracy varies by domain—factual business news scores higher than opinion pieces with complex irony or sarcasm.
- Yes, our entity-level sentiment analysis identifies how specific brands, people, or organizations are portrayed within articles. An article might be neutral overall but contain positive sentiment about one company and negative about another.
- Our advanced NLP models are trained to recognize common sarcasm patterns and contextual irony. While no system is perfect with nuanced language, we achieve strong performance by analyzing surrounding context, source characteristics, and topic-specific language patterns.
- Many financial firms use news sentiment as one input for trading models. Our API provides real-time sentiment data that can be correlated with market movements, though we recommend combining sentiment signals with other fundamental and technical analysis.
- Yes, our sentiment models support 60+ languages with language-specific training. Each model understands cultural nuances and language-specific expressions that affect sentiment interpretation, ensuring accurate analysis across global news sources.
- Use our time-series endpoints to aggregate sentiment data by hour, day, week, or month. Track how sentiment around topics, brands, or events evolves, and correlate changes with specific news events or campaigns.
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