Sentiment Analysis in Action with Apache Flink: Building Your Real-time Pipeline

Monitoring and interpreting sentiment of data records is important for a variety of use cases. However, traditional human-based methods fall short in handling huge volumes of information with required speed and efficiency. Yet AI can.

AI, however, is only part of the solution. We’ll need to build a data pipeline that ingests data from various channels, processes it using AI-driven sentiment analysis models to classify the sentiment of each individual record and get ready to be consumed by applications for aggregation and analysis.

Together in this session we'll build a system using open source technologies Apache Kafka and Apache Flink with AI models to get real-time sentiment from social media data. Apache Kafka's scalability ensures that no record is left behind, making it a reliable foundation for sentiment analysis. Apache Flink, with its adaptability to fluctuations in data volume and velocity, will enable the analysis of a continuous data stream using an AI model.