Step-by-Step Tutorial for Emotion Detection, Sentiment Analysis, and LLM-Based NLP Using OpenAI, including Evaluation and Cost Estimation
11 min read12 hours ago
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Introduction
Emotion classification from social media text is one of the most practical applications of natural language processing. Whether you’re analyzing brand sentiment, understanding user feedback, o…
Step-by-Step Tutorial for Emotion Detection, Sentiment Analysis, and LLM-Based NLP Using OpenAI, including Evaluation and Cost Estimation
11 min read12 hours ago
–
Press enter or click to view image in full size
Non-members read here for free.
Introduction
Emotion classification from social media text is one of the most practical applications of natural language processing. Whether you’re analyzing brand sentiment, understanding user feedback, or building mental health monitoring tools, the ability to programmatically classify emotions at scale is invaluable.
In this comprehensive guide, I’ll walk you through building a production-ready emotion classification system using OpenAI’s GPT-5-nano model. We’ll go beyond binary sentiment analysis and classify tweets into 5 distinct emotions: anger, disgust, happiness, surprise, and sadness.
By the end of this article, you’ll understand:
- How to structure prompts for reliable emotion classification
- How to integrate with OpenAI’s API effectively
- How to evaluate model performance with practical metrics
- How to estimate costs for scaling to production