Social Value Evaluation System Using Sensitivity Labeling
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Solution Overview
Problem
Existing methods for evaluating social value from text data, such as those used by companies and local governments, are inefficient and lack effective utilization of sentiment and opinion data from general users, requiring specialized knowledge and failing to provide actionable insights for improving social value.
Innovation Solution
A computer system that analyzes input text data using a sensitivity labeling model to categorize messages and calculate correlations with predetermined indices, enabling the evaluation of social value by categorizing messages into sensitivity levels and correlating them with ESG scores, providing actionable insights for improving social value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If full-text search using keywords is used to extract desired information from large amounts of text data, then the search can cover a broad range of data, but it takes time for users to examine the enormous amount of data to extract useful information
Solution Approach 1:
The patent replaces manual keyword search and examination with an automated machine learning-based sentiment analysis system. The system automatically processes text data, performs sentiment analysis, and generates evaluation results without requiring users to manually examine large amounts of data, thus substituting mechanical human effort with automated computational processing.
Solution Approach 2:
The system enables self-service by automatically analyzing text data and generating sentiment evaluation results without requiring user intervention in the examination process. The machine learning model autonomously processes the data, categorizes sentiments, and produces actionable insights, allowing the system to serve itself in extracting useful information from large datasets.
2Measurement precision
If machine learning methods are used to identify related words and search for similar sentences, then texts closer to desired information can be extracted, but specialized knowledge and experience are required to execute the analysis
Solution Approach 1:
The patent introduces an intermediary sentiment analysis system that bridges the gap between raw text data and actionable insights. This intermediary system automatically performs the complex analysis tasks, including word relationship identification and sentence similarity search, eliminating the need for users to possess specialized knowledge while maintaining high extraction precision through trained machine learning models.
Solution Approach 2:
The system changes the parameters of text analysis by using pre-trained machine learning models with predefined sentiment categories and evaluation criteria. This transforms the complex task of specialized text analysis into a standardized process that automatically adjusts parameters such as sentiment thresholds and categorization weights, making the analysis executable without specialized expertise while maintaining precision.
3Measurement precision
If evaluation indices such as ratings are used to evaluate social value, then quantitative evaluation can be performed, but details of the evaluation criterion process are not disclosed and it is not known whether general user opinions are reflected
Solution Approach 1:
The patent implements feedback by systematically collecting and analyzing general user opinions from text data and incorporating them into the evaluation process. The machine learning model processes user-generated content, extracts sentiment information, and feeds this back into the evaluation index calculation, ensuring that general user perspectives are reflected in the final social value assessment while maintaining quantitative precision.
Solution Approach 2:
The system performs preliminary actions by pre-processing text data to extract sentiment information and user opinions before generating the final evaluation results. This preliminary analysis of general user feedback is conducted in advance, allowing the system to transparently incorporate diverse perspectives into the quantitative evaluation framework while maintaining measurement precision through structured analysis.
Data Source
AI summary
To provide a computer system capable of evaluating a social value included in text data, it is provided an evaluation system for evaluating a predetermined index based on an input message being input text information, the evaluation system comprising: a calculation unit configured to execute calculation processing; and a storage unit accessible from the calculation unit, the calculation unit including: an analysis module configured to analyze the input message through use of a model that adds a label of sensitivity to the input message, to thereby categorize the input message; and an evaluation module configured to calculate correlation between a numerical value of each item included in a result of the analysis obtained by the analysis module and a predetermined index value.