01
Why Sentiment Analysis Matters
Understand customer emotions from reviews, tweets, and comments. Move from guessing to data-driven decisions: spot trends, improve products, and craft better messages.
Business impact
Detect satisfaction, save churn, refine product roadmap and marketing.
Channels
E-commerce reviews, social media, customer support transcripts.
Outcomes
Faster decisions, prioritized fixes, targeted campaigns, improved CX.
Design goal
Turn raw text into measurable sentiment and confidence scores.
02
What Spring AI Brings
A Spring-native approach to integrating AI models into Java applications. Works with pre-trained backends or custom models and fits into Spring Boot apps easily.
Integration
Seamless with Spring Boot and existing systems.
Flexibility
Support for multiple AI backends and custom models.
Production-ready
Configuration, stability, and scaling patterns included.
Developer fit
Gentle learning curve for teams that already use Spring.
03
How to build a Sentiment Service
Create a small service that sends text to an AI model and returns a label plus confidence. Keep responsibilities separated: service layer + REST controller.
Service
Calls the model, formats prompts, returns sentiment result.
API
Expose a REST endpoint to accept text and return JSON results.
Prompting
Ask for label (positive/negative/neutral) and confidence; prefer structured JSON responses.
Testing
Unit tests + mocked model responses for reliable CI testing.
04
Advanced & Scale
Return structured objects (text, label, confidence). For scale, use async processing, message queues, caching, and monitor model performance.
Structured response
Include fields like text, label, confidence, timestamp, language.
Scaling
Kafka/RabbitMQ for batches; async calls; caching of common queries.
Monitoring
Track accuracy drift and latency; update models as language shifts.
Extensibility
Add emotion categories (joy, anger) beyond polarity.