LDA Literature Review Workflow for Topic Trend and Correlation Analysis
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Solution Overview
Problem
Existing Latent Dirichlet Allocation models struggle with analyzing heterogeneous documents and lack efficiency in terms of time and cost when reviewing large-volume literature, without providing numerical values for topic trends and correlations.
Innovation Solution
A device and method using an enhanced Latent Dirichlet Allocation model that includes data collection, pre-processing, modeling, clustering, interest analysis, generality analysis, similarity analysis, and network analysis units to automatically classify topics, update topic numbers, and generate probability distributions, enabling efficient review of large-volume literature with quantitative trend, scope, and correlation confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Latent Dirichlet Allocation models are used for topic analysis, then topic classification can be performed, but the analysis of heterogeneous documents is difficult and the process is inefficient in terms of time and cost
Solution Approach 1:
The patent segments the literature review process into distinct functional modules: data collection unit, pre-processing unit, modeling unit, clustering unit, interest analysis unit, generality analysis unit, similarity analysis unit, network analysis unit, and display unit. Each module handles a specific aspect of the analysis, enabling parallel processing and improving overall efficiency while maintaining manageable complexity through modular design.
Solution Approach 2:
The enhanced LDA model integrates multiple analysis functions into a single unified system that can simultaneously perform topic classification, trend analysis, scope analysis, similarity analysis, and correlation analysis. This multi-functional approach eliminates the need for separate analysis tools and improves productivity by providing comprehensive literature review capabilities in one system.
2Loss of information
If traditional topic analysis methods are used, then basic topic classification is possible, but quantitative analysis of topic trends, scope, similarity, and correlation is not provided
Solution Approach 1:
The patent extends traditional topic analysis by adding quantitative dimensions: temporal dimension (trend analysis over time), scope dimension (generality analysis), similarity dimension (distance between topics), and correlation dimension (relationship between topics). These additional dimensions transform qualitative topic classification into comprehensive quantitative analysis, providing complete information without excessive complexity through systematic mathematical formulations.
3Loss of time
If manual literature review is performed, then detailed analysis is possible, but it is time-consuming and costly for large-volume literature
Solution Approach 1:
The patent creates a computational model that replicates and automates the manual literature review process. The enhanced LDA model processes large volumes of literature data automatically, generating quantitative measurements of topic trends, scope, similarity, and correlation that match the precision of manual analysis while eliminating time and cost constraints. The system processes text data through algorithmic operations that maintain analytical rigor at scale.
Data Source
AI summary
A device and method for reviewing literature by using Latent Dirichlet Allocation (LDA) is proposed. The device may include a pre-processing unit extracting text data for modeling, and a modeling unit automatically classifying topics as many as a set number (K) and generating a probability distribution of the topics by literature and a probability distribution for words by topic. The device may also include a clustering unit updating the number (K) of the topics, an interest analysis unit confirming trends by topic over time, and a generality analysis unit quantitatively confirming a research scope of each specific topic. The device may further include a similarity analysis unit quantitatively confirming a similarity between the topics, a network analysis unit quantitatively confirming a correlation between the topics, and a display displaying the trends by topic over time, research scope of each specific topic, similarity between the topics, and correlation between the topics.