Introduction
Customer feedback contains valuable signals about satisfaction, recurring concerns, and areas of improvement. In this walkthrough, we demonstrate how to build a reusable sentiment analysis solution using Oracle AI Data Platform.
Starting with a customer reviews dataset, the notebook classifies sentiment, calculates confidence scores, identifies key topics, and prepares the results for analysis in Oracle Analytics.
Key Highlights
- Configure the source dataset, output table, review text column, sentiment model, and analysis levels from a single notebook configuration section.
- Classify customer feedback as Positive, Negative, or Neutral with confidence scores.
- Analyze sentiment at Document, Sentence, Aspect, and Topic levels.
- Extract meaningful aspects and business-friendly topics from unstructured text.
- Store the results in an Oracle AI Data Platform Bronze Catalog.
- Analyze the results in Oracle Analytics for visualization, trend analysis, and root-cause analysis.
Reference
Watch the Demo
Demo Video: Perform Sentiment Analysis in Oracle AI Data Platform
Check the notebook
Accompanying Guide
This solution can be adapted to different datasets and business requirements by updating the notebook configuration without changing the core processing logic.
This approach enables organizations to identify customer concerns, monitor sentiment trends, compare business entities, and understand the factors driving customer satisfaction and dissatisfaction.
