Sentiment Analysis Portability Detection via Confidence Score Distribution
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Solution Overview
Problem
Existing sentiment analysis models require manual and reactive intervention to determine portability across different data sets or domains, which is tedious, time-consuming, and not scalable, as they lack an automated method to assess accuracy changes and decide on retraining needs.
Innovation Solution
A system that computes confidence score distributions for sentiment analysis models, using a benchmark data set, and applies the Kolmogorov-Smirnov test to determine the significance of changes, automatically determining portability and recommending retraining if changes exceed a threshold, thereby facilitating proactive and automatic portability analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and reactive intervention is used to determine model portability, then accuracy assessment can be performed, but the process becomes tedious, time-consuming, and not scalable
Solution Approach 1:
The system enables automatic self-assessment of model portability by comparing confidence score distributions between benchmark and target data sets. The portability determination is performed autonomously without requiring manual administrator intervention, allowing the system to self-evaluate whether a trained sentiment analysis model can be ported to new data sets while maintaining accuracy standards
Solution Approach 2:
The patent replaces manual administrative processes with an automated statistical testing system. The Kolmogorov-Smirnov test automatically compares confidence score distributions and determines portability based on statistical significance, substituting the mechanical manual review process with an automated computational system that scales efficiently
2Productivity
If automated portability detection is implemented, then scalability is improved, but the system complexity increases due to statistical testing requirements
Solution Approach 1:
The patent introduces confidence score distributions as an intermediary layer between the sentiment analysis model and the portability determination process. Instead of directly analyzing model outputs, the system uses confidence scores as a mediator that captures model certainty, allowing statistical comparison to determine portability while maintaining system modularity and manageability
Solution Approach 2:
The system transforms the portability assessment problem into a parameter comparison task by changing from direct model output analysis to confidence score distribution analysis. This parameter transformation enables the use of statistical tests while simplifying the overall assessment process, as confidence scores provide a standardized metric that can be systematically compared across different data sets
Data Source
AI summary
Embodiments of the present invention provide a system that that can be used to determine whether a sentiment analysis model is portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates the accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes.