Sentiment Analysis Using Keyword Hierarchy for Granular Scoring
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Solution Overview
Problem
Current sentiment analysis methods fail to recognize sentiment expression relationships, lacking granularity in analyzing large amounts of data from various sources such as social media and message boards, which limits enterprises' ability to understand public opinion effectively.
Innovation Solution
A sentiment-scoring system that includes a storage device for keywords and keyword groups, with a processor to identify keywords, determine associated keyword groups, and calculate sentiment scores within a hierarchical structure, allowing for detailed analysis and presentation of sentiment metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentiment expressions are analyzed one-by-one, then the analysis process is simple, but the granularity and depth of understanding sentiment relationships is insufficient
Solution Approach 1:
The patent segments the analysis process into multiple hierarchical levels (keyword level, keyword group level, and hierarchy level) to enable granular sentiment analysis. Each level provides a different scope of analysis, allowing the system to maintain simplicity at each segment while achieving comprehensive precision across all levels.
Solution Approach 2:
The patent introduces a hierarchical dimension to the analysis structure, organizing keyword groups into parent-child relationships that create multiple analysis dimensions. This dimensional approach allows the system to analyze sentiment at different levels of abstraction simultaneously, improving granularity without proportionally increasing complexity.
2Quantity of substance
If large amounts of data from multiple sources are collected, then the comprehensiveness of public opinion analysis is improved, but the difficulty of processing and analyzing the data increases
Solution Approach 1:
The patent merges multiple data sources and analysis operations into a unified hierarchical framework. By combining keyword identification, keyword grouping, and hierarchy organization into a single integrated system, the patent reduces the difficulty of processing large volumes of data from multiple sources while maintaining comprehensive analysis capability.
Solution Approach 2:
The hierarchical structure serves multiple functions simultaneously: it organizes keywords, groups related concepts, establishes relationships between entities, and enables multi-level analysis. This multi-functionality reduces processing difficulty by consolidating multiple operations into a single universal framework that handles diverse data types.
3Measurement precision
If detailed keyword hierarchy structure is implemented, then the granularity of analysis is improved, but the complexity of the system structure increases
Solution Approach 1:
The patent implements a nested hierarchical structure where keyword groups are organized within parent categories, creating a nested arrangement that maintains precision at each level while managing overall system complexity. The nested structure allows detailed analysis at leaf nodes while providing high-level overview capabilities at root nodes.
Solution Approach 2:
The patent performs preliminary organization of keywords into groups and hierarchies before actual sentiment analysis occurs. This preliminary structuring reduces the complexity of real-time analysis by pre-establishing the framework, allowing the system to focus computational resources on the actual sentiment scoring rather than data organization.
Data Source
AI summary
A sentiment-scoring system may include a storage device configured to store a plurality of keywords, keyword groups, and a keyword group hierarchy. Each keyword may be associated with at least one of the keyword groups. The keyword hierarchy may include a hierarchy associated with each keyword group. The system may further include a processor in communication with the storage device. The processor may be configured to locate a plurality of sentiment expressions and identify a plurality of keywords present in the plurality of sentiment expressions. The processor may be further configured to determine at least one respective keyword group associated with each identified keyword and determine a sentiment score for each sentiment expression with respect to the associated keyword group within the keyword hierarchy. The processor may be further configured to provide at least one sentiment score to a display. A method and computer-readable medium may also be implemented.