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.
Olena Kutsenko
Olena is passionate about data and its applications, sustainable software development and teamwork. With a background in computer science, she's led teams and developed mission-critical applications at Nokia, HERE Technologies, and AWS. Currently, she works at Aiven where she supports developers and customers in using open-source data technologies such as Apache Kafka, ClickHouse, and OpenSearch. She is also an international public speaker and regularly present at conferences around the world. She is a Confluent Catalyst.