Generative Model for User-Modified Topic and Sentiment Analysis
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Solution Overview
Problem
Existing methods for joint topic and sentiment analysis in text require substantial training data, which can be scarce, leading to inefficient processing and model development.
Innovation Solution
A generative model that allows user input to initiate or modify topic and sentiment models, reducing the need for extensive training data by incorporating expert acumen and enabling quicker convergence to final models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional topic and sentiment modeling techniques are used, then models can be developed with automated processing, but substantial training data is required which may be scarce
Solution Approach 1:
The patent applies preliminary action by allowing users to pre-define topic models and sentiment models before actual text analysis. Users can manually create topic structures, define sentiment lexicons, and configure model parameters in advance, reducing the dependency on large training datasets during the modeling phase.
Solution Approach 2:
The patent introduces an intermediary layer between raw text and analysis results through user-configurable topic and sentiment models. This intermediary allows users to guide the analysis process with domain knowledge, acting as a mediator that reduces the need for extensive training data while maintaining analysis quality.
2Measurement precision
If extensive training data is used to develop topic and sentiment models, then model accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent enables self-service modeling where users can independently create and configure topic and sentiment models without requiring extensive automated training processes. The system provides tools for users to manually define models, adjust parameters, and iterate quickly, reducing both development time and computational resource requirements while maintaining acceptable accuracy.
3Productivity
If automated topic and sentiment modeling is performed without user input, then processing efficiency is maintained, but model relevance to specific domains decreases
Solution Approach 1:
The patent implements dynamics by allowing the system to operate in multiple modes: fully automated processing for efficiency, and user-configurable modes for improved relevance. Users can dynamically adjust the level of manual configuration based on their needs, enabling the system to adapt between speed and accuracy requirements for different analysis tasks.
Data Source
AI summary
A generative model is used to develop at least one topic model and at least one sentiment model for a body of text. The at least one topic model is displayed such that, in response, a user may provide user input indicating modifications to the at least one topic model. Based on the received user input, the generative model is used to provide at least one updated topic model and at least one updated sentiment model based on the user input. Thereafter, the at least one updated topic model may again be displayed in order to solicit further user input, which further input is then used to once again update the models. The at least one updated topic model and the at least one updated sentiment model may be employed to analyze target text in order to identify topics and associated sentiments therein.