テキストの感情を決定する と ニュースAPI

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.

はい、当社のエンティティレベルの感情分析は、特定のブランド、人物、または組織が記事内でどのように描写されているかを特定します。記事全体としては中立かもしれませんが、ある企業に対しては肯定的な感情を、別の企業に対しては否定的な感情を含んでいる可能性があります。

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.