Event Intensity Assessment via Sentiment and Impact Analysis

The patent replaces manual human-based information prioritization with an automated computer system that uses machine learning models to detect events, determine sentiment and impact, and assess intensity. The system processes textual input from multiple sources automatically, eliminating the need for human analysts to manually review and prioritize information while reducing subjectivity and oversight errors.

The system performs self-service by automatically detecting events, classifying them by type and intensity, and generating responses without requiring continuous human intervention. The machine learning models continuously process incoming information and autonomously determine which events warrant attention and what actions should be taken, enabling the system to operate independently at scale.

The patent segments the information processing workflow into distinct functional components: event detection, event classification by type, sentiment analysis, impact assessment, and response generation. Each component processes specific aspects of the information independently, allowing the system to handle vast volumes of data from multiple sources efficiently by dividing the complex processing task into manageable segments that can operate in parallel.

The system performs preliminary actions by pre-classifying events into types and pre-assessing their intensity levels before generating responses. The machine learning models continuously train and update their classification criteria, enabling rapid processing of new information without requiring re-evaluation of all previous data. This preliminary categorization allows the system to quickly identify and prioritize the most critical events from the vast information volume.