AI Feedback Classifier – Text Categorization in Real-Time

Project Summary

The AI Feedback Classifier classifies customers' feedback automatically based on sentiment analysis and topic detection through a completely serverless AWS implementation. It is an example of how natural language processing abilities are elegantly combined with cloud native design principles, and where scalability, economics, and business practicality come into play.

The system works on texts in real-time, giving instantaneous indications of customer sentiment and trends of feedback without the need for infrastructure management.

Core Capability

Intelligent Text Analysis: Uses Amazon Comprehend to conduct sentiment analysis and derive key topics from customer feedback, yielding business-relevant insights.

Serverless Architecture: Entirely built on AWS serverless services such as API Gateway for request processing, Lambda for logic execution, and DynamoDB for data storage.

Auto-Scaling Infrastructure: It automatically manages fluctuating traffic volumes without capacity planning or manual intervention.

Real-Time Processing: Facilitates on-the-spot feedback categorization, enabling organisations to react instantly to issues and opportunities from customers.

System Design

The application uses a three-tier serverless architecture:

API Layer: Amazon API Gateway is the entry point, and it receives feedback submissions and forwards requests to the processing layer.

Compute Layer: AWS Lambda functions orchestrate the workflow by invoking Amazon Comprehend services for text analysis and managing data flow.

Data Layer: DynamoDB stores the outcomes of processing with original review texts, sentiment values, confidence, and category topics extracted.

Analytics Layer (Optional): Points of integration for Amazon Athena and QuickSight allow sophisticated querying and visualizations of data against categorized feedback.

Technical Implementation

AWS Services: API Gateway, Lambda, DynamoDB, Amazon Comprehend, IAM, CloudWatch

Programming Language: Python for Lambda function development

Infrastructure Management: Consistent, Repeatable Deployments Using CloudFormation Templates

Monitoring: Merging with CloudWatch for logging, metrics, and performance monitoring

Problem-Solving Approach

Optimization of Data Structure: Designed a DynamoDB schema that can efficiently query based on sentiment category and score ranges, enabling fast retrieval in support of analytics and reporting.

Pipeline Validation: Carried out extensive testing using Postman and ready-to-use datasets to test the complete data flow from API Gateway down through Lambda, Comprehend, and DynamoDB before rolling out into production.

Error Handling: Built robust exception handling throughout the pipeline to manage API limits, service timeouts, and data validation scenarios.

Major Learning Outcomes

• Acquired skills in incorporating AWS AI/ML services within production pipelines, with hands-on familiarity with Amazon Comprehend's functionality and constraints.

• Better understanding of serverless patterns in applications, including event-driven patterns and stateless processing patterns.

• Improved debugging and monitoring skills on distributed systems, learning how to trace requests across various AWS services and locate performance bottlenecks.

Scheduled Enhancements

Interactive Frontend: Develop a Streamlit-based web interface allowing users to input feedback and view real-time classification results with visual sentiment indicators.

Multilingual Capability: Implement Amazon Translate to allow the processing of feedback in languages other than English, enhancing the global relevance of the application.

Business Intelligence Incorporation: Develop detailed dashboards with Amazon QuickSight to offer stakeholders trend analysis, sentiment monitoring, and actionable recommendations.

Advanced Analytics: Use custom trend detection and topic modeling algorithms to discover new trends in customer feedback over time.

Project Resources

Source Code: AI Feedback Classifier GitHub Repo