Sentiment Analysis

Sentiment analysis detects how people feel from their text. At its simplest, it classifies text as positive, negative, or neutral. More advanced analysis detects specific emotions (joy, anger, frustration, excitement), intensity levels, and aspect-based sentiment (positive about the product but negative about the price). With modern LLMs, sentiment analysis is trivially easy. Send text to GPT-5-mini with the prompt "Classify the sentiment of this text as positive, negative, or neutral. Return JSON: {sentiment, confidence, reasoning}" and you get accurate results. For high-volume processing, fine-tuned smaller models or traditional ML classifiers are more cost-effective. Production use cases: monitoring app store reviews (alert on negative sentiment spikes), analyzing customer support conversations (escalate frustrated users), social media monitoring (track brand perception), and content moderation (flag emotionally charged content). The key is acting on the analysis: a sentiment score alone is useless; pair it with alerts, routing, or automated responses.