Mood State Determination Using Text Segmentation and Weighting
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Solution Overview
Problem
Current sentiment analysis techniques lack depth and accuracy in determining mood states beyond simple positive, negative, or neutral sentiments, and require complex linguistic rules that are time-consuming to develop and often fail to differentiate between varying degrees of sentiment, such as depression, fear, or anger.
Innovation Solution
A computer-implemented system that identifies and maps text segments in documents to a predetermined set of mood scales, assigning mood weights to generate an overall mood score, using linguistic modifiers like negations, amplifiers, and dampers to refine mood classification, providing a more comprehensive and reusable analysis across different projects and industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple sentiment analysis (positive, negative, neutral) is used, then the analysis process is fast and easy to implement, but the depth and accuracy of mood state determination is insufficient
Solution Approach 1:
The patent segments the document into multiple text segments and analyzes each segment individually to determine mood indicators. This segmentation allows the system to capture nuanced mood states in different parts of the document while maintaining an organized analysis process that balances depth with manageability.
Solution Approach 2:
The patent introduces multiple mood scales with different parameters (mood type, intensity, polarity) to transform the analysis from simple sentiment classification to multi-dimensional mood state determination. This parameter expansion enables more precise mood measurement without requiring overly complex system architecture.
2Measurement precision
If complex linguistic rules are developed to differentiate mood states, then the analysis depth improves, but the development time and complexity increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining mood scales, mood indicators, and mapping relationships between text segments and moods. This preliminary structuring enables the system to differentiate mood states efficiently during actual analysis without requiring complex rule development at runtime, reducing both development time and analysis complexity.
3Measurement precision
If multiple mood scales and weights are introduced, then the nuance and detail of mood analysis improve, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by assigning different mood weights to different text segments based on their specific context and importance. This localized weighting approach allows the system to achieve precise mood scoring without uniformly complex processing across the entire document, maintaining processing efficiency while improving accuracy.
Data Source
AI summary
Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.