Real-Time Sentiment Analysis of Comments and Reviews: A Kafka Driven Approach

Buyers make desired decisions based on reviews available on E-commerce platforms (i.e., the ratings and an overview of pertinent item text are used for decision making). E-commerce giants like Amazon and Flipkart provide a platform that allows customers to share their experiences, offering real insights about product performance to future buyers. People trust reviews, and Amazon customers often check online reviews before purchasing a product. A high number of reviews suggests that a product is popular, while numerous positive reviews may indicate its high quality. To extract valuable insights from an extensive collection of reviews, grouping reviews into positive and negative sentiments is necessary. Sentiment Analysis, the process of detecting positive or negative sentiments in text, is commonly used by businesses to assess social data, gauge brand reputation, and understand customers. To enhance the existing sentiment analysis, a real-time sentiment analysis pipeline will be introduced using Message Queues as the backbone for event-driven processing. Instead of batch processing reviews, the new system will stream customer reviews in real time, allowing for near-instant classification and analysis. This will involve multiple steps: Real-time data ingestion from various sources (e-commerce platforms, social media) using Kafka producers. Stream processing, Trend Monitoring, Scalability and fault tolerance enabled by Kafka, ensuring the system can handle high data throughput from diverse sources.