Glossary term
Glossary term
Foundations
NLP task of classifying the emotional polarity (positive, negative, neutral) or detailed sentiment of text.
Using statistical or machine learning algorithms to determine a group's overall attitude—positive or negative—toward a service, product, organization, or topic. For example, using natural language understanding, an algorithm could perform sentiment analysis on the textual feedback from a university course to determine the degree to which students generally liked or disliked the course.
See the Text classification guide for more information.
Twitter (X) uses sentiment analysis to power its trending-topics algorithm and ad-relevance scoring, processing 500 million tweets per day to extract brand sentiment signals for advertisers.
Medallia's AI platform uses aspect-based sentiment analysis to extract sentiment toward specific product attributes (battery life, UI, customer service) from 100M+ customer reviews, giving companies granular product improvement signals.
JP Morgan's NLP research team developed a financial sentiment model (FinBERT) fine-tuned on 10,000 financial news articles, achieving 89% accuracy on market-moving sentiment classification, outperforming general-purpose BERT by 15%.
Created for this library
A retail analytics team uses sentiment analysis on customer reviews to track satisfaction trends by product category.
A bank's compliance team uses sentiment analysis on call transcripts to surface dissatisfied customers for follow-up.
A media company uses sentiment analysis on social mentions to monitor public reaction to its content in near-real time.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License