What is the difference between polarity and sentiment 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.

How accurate is your sentiment analysis across different topics?

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.

Kan jeg analysere stemningen for specifikke enheder inden for en artikel?

Ja, vores sentiment-analyse på enhedsniveau identificerer, hvordan specifikke mærker, personer eller organisationer fremstilles i artikler. En artikel kan være neutral overordnet, men indeholde positiv sentiment om et selskab og negativ om et andet.

Hvordan håndterer du sarkasme, ironi og nuanceret sprog?

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.

Kan jeg bruge sentimentanalyse til aktiemarkedsforudsigelser?

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.

Does sentiment analysis work for non-English content?

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.

How can I build sentiment trend reports over time?

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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Bestemmelse af teksternes følelser med Nyheder API

Last updated: May 30, 2026

Tilmeld dig gratis og begynde at bestemme følelser af tekster med vores nyheder API. Få adgang til de seneste nyhedsartikler.

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Bestemmelse af følelser af tekster med nyheder API funktioner

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.

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Use cases

Oplev mulighederne

Vores løsninger er designet til at hjælpe dig med at nå dine mål. Uanset om du er en lille virksomhed eller en stor virksomhed, har vi den rigtige løsning til dig.

Training-data augmentation

Bootstrap labeled datasets using APITube's sentiment scores; refine with human review on a smaller sample.

Trading

Signal pipelines

Plug per-article sentiment polarity and intensity into quant signal pipelines and backtests.

Produkt

In-app sentiment overlays

Surface sentiment badges next to news articles in news-reader, finance, or analytics apps.

Research

Topic-sentiment dashboards

Build dashboards showing sentiment by topic, geography, and publisher tier for any custom domain.

Operations

Internal-tool enrichment

Enrich internal news feeds (Slack, Teams, intranet) with sentiment labels so analysts triage faster.

Validation

Cross-source consensus scoring

Compare sentiment across multiple sources on the same event to detect outliers or framing differences.

Funktion

Spild ikke tid på komplekse funktioner

Komplekse funktioner gjort enkel. Vores API giver en enkel måde at få adgang til nyhedsartikler fra hele verden. Vi leverer en enkel, konsekvent og nem at bruge API til at få adgang til nyhedsartikler fra tusindvis af kilder.

Nyheder API

Eksporter data i mange formater
Industriovervågning
Brand Overvågning
Markedsinformation
Risikostyring
Konkurrencespionage
Medieovervågning
Sentimentanalyse
Tendensanalyse
Historie Gruppering
Prognoser for sociale tendenser
Understøttelse af flere sprog
Publikums Engagement
Geografisk Analyse
Breaking News i realtid
Historisk dataadgang
Custom News Feeds
News Aggregation
Content Filtering
Over 50 integrationer

Uddrag Yderligere Data

Industri
Beliggenhed
Person
Organisationer
Brands
Events
Disasters
Diseases
Link
Medier
Images & Videos
Hashtags
Forfattere
Kilde
Duplicate Detection
Udgiver Rang
Artikelsentiment
Readability Score
Language Detection
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Analyse

Sentimentanalyse
Analyse af den offentlige mening
Kategorisering
Finansiel Analyse
Tendensanalyse
Historie Gruppering
Indholdsoversigt
Anerkendelse Af Enheder
Søgeordsekstraktion
Emne Modellering
Hændelsesdetektering
Anerkendelse Af Navngivne Enheder
Tekstklassificering
Controversy Detection
Trust Score Analysis
Engagement-målepunkter
Registrering af kildeforskydning
Kvalitetsrangering
Spam-registrering
Følelsesregistrering

Avanceret Søgning

Søg efter sted
Søg efter datointerval
Søg efter kilde
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Søg efter branche
Søg efter Sentiment
Søg efter historie
Søg efter Publisher Rank
Søg efter sprog
Søg efter enhed
Søg efter nøgleord
Boolsk søgning
Nærhedssøgning
Facetteret søgning
Rækkeviddeforespørgsler
Søg efter forfatter
Søg efter medieType
Søg efter Breaking News
Søg efter læsetid
+ over 65 flere filtre

Interaktiv demonstration

Vælg et scenario og se rigtige API-svar — filtreret, struktureret og klar til brug. Ingen legitimationsoplysninger påkrævet.

News API Scenarier

Klar til at bygge med News API?

Opdag opskrifter og kodeeksempler for almindelige nyhedsdataworkflows.

Workflow-eksempler

Full data models

API Response

✓ Tesla Stock Sentiment

Status: ok

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Key Features Demonstrated:

Sentiment AnalysisCompany ExtractionRich MetadataImage URLsSource Attribution

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Ofte stillede spørgsmål

: 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.
: Ja, vores sentiment-analyse på enhedsniveau identificerer, hvordan specifikke mærker, personer eller organisationer fremstilles i artikler. En artikel kan være neutral overordnet, men indeholde positiv sentiment om et selskab og negativ om et andet.
: 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.