Deep Content Classification for Brand Sentiment Tracking

The patent introduces a sentiment analysis system as an intermediary between web advertising platforms and brand performance tracking. This system automatically analyzes multimedia content (images, videos, text) across web platforms to extract sentiment information, enabling real-time monitoring without directly modifying the advertising platforms themselves. The intermediary processes vast amounts of web content and converts it into actionable sentiment metrics.

The patent replaces manual sentiment analysis methods with automated computational systems. Instead of human analysts manually evaluating brand sentiment across web platforms, the system uses algorithms to automatically process multimedia content, generate signatures, and determine sentiment polarity. This substitution enables real-time analysis at scale, resolving the contradiction between broad advertising reach and precise sentiment measurement.

The patent creates a universal sentiment analysis system that can process multiple types of multimedia content (images, videos, text) across different web platforms through a single unified interface. The system uses common signature generation techniques that work across diverse content types and platforms, reducing the complexity that would otherwise arise from needing separate analysis tools for each platform and content type.

The patent transforms complex multimedia sentiment analysis into a standardized parameter-based system. By converting diverse content into numerical signatures and sentiment polarity values, the system simplifies the analysis process. The transformation of various content types into uniform parameters (signatures, sentiment scores) enables consistent analysis across multiple platforms without proportionally increasing complexity.

The patent extracts only the essential features needed for sentiment analysis from vast amounts of multimedia content. Instead of processing entire videos, images, or text documents, the system generates compact signatures that capture the essential sentiment-relevant characteristics. This extraction approach enables real-time analysis by reducing computational requirements while maintaining analysis accuracy.

The patent implements sentiment analysis at a partial level rather than attempting complete comprehensive analysis of all content attributes. By focusing specifically on sentiment-relevant features and using signature-based matching, the system achieves practical real-time monitoring without the excessive computational resources that would be required for exhaustive content analysis. The approach performs sufficient analysis for the intended purpose without unnecessary computational overhead.