Automated Topic Analytics for Online Articles

The patent replaces manual mechanical analysis with automated computational text analysis. The system uses computer algorithms to extract topics from article text, calculate relevance scores, and determine topic popularity based on visitor metrics, eliminating the need for manual reading and subjective judgment while providing objective, scalable measurements.

The patent introduces an intermediary computational system that bridges articles and visitor data to derive topic popularity. This intermediary process calculates relevance scores by analyzing the relationship between topic keywords and article content, then combines these scores with visitor metrics to produce objective topic popularity measurements without direct human intervention.

The patent segments the analytics process into distinct functional components: topic extraction from article text, relevance score calculation, visitor metric integration, and aggregated topic popularity computation. This segmentation allows the system to transform existing article-level data into topic-level insights through a structured multi-step process while maintaining modularity.

The patent adds a new dimension of analysis by extracting semantic topic information from article text and combining it with existing visitor metric dimensions. This creates a topic-level aggregation layer that transforms one-dimensional article popularity data into multi-dimensional topic popularity insights, enabling publishers to see patterns across multiple articles.