Neural Network Topic Extraction Using Contextual LDA
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Solution Overview
Problem
The challenge lies in efficiently analyzing user-generated textual and voice data to extract actionable topics from vast, dynamic, and contextually complex online content, particularly in identifying recurring issues relevant to specific locations and communities in real-time, due to the absence of prior information and the difficulty in verifying accuracy and relevance.
Innovation Solution
A method involving training neural network models to generate distributed vector representations, computing relevance matrices using cosine distance, and performing latent Dirichlet allocation (LDA) to refine topic distributions, along with extracting location and time metadata, enables the identification of actionable topics and remedial actions from user-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text analysis methods are used on user-generated content, then the analysis process is simple, but the ability to accurately identify actionable topics and verify accuracy deteriorates due to lack of context
Solution Approach 1:
The patent introduces context as an intermediary element that mediates between user-generated content and topic identification. By incorporating contextual information from multiple sources (social connections, location, time), the system achieves more accurate topic identification without requiring overly complex analysis mechanisms. The context acts as a bridge that enriches the analysis input.
Solution Approach 2:
The patent transitions from traditional single-dimension text analysis to multi-dimensional analysis by incorporating contextual dimensions (social, spatial, temporal). This dimensional expansion allows the system to identify actionable topics more accurately by considering multiple facets of the data simultaneously, rather than relying solely on text content.
2Reliability
If comprehensive analysis of all user-generated content is performed, then complete topic coverage is achieved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent segments the analysis process into distinct components: content analysis, context analysis, and topic identification. By dividing the comprehensive analysis into manageable segments that can be processed independently and parallelized, the system maintains complete topic coverage while reducing overall analysis time and computational resource requirements.
Solution Approach 2:
The patent performs preliminary context analysis and metadata extraction before the main topic identification process. By preparing contextual information in advance (social connections, location, time stamps), the system reduces the computational burden during the actual topic analysis phase, achieving both completeness and efficiency.
3Speed
If real-time analysis of user-generated content is implemented, then timely issue identification is achieved, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent implements periodic analysis cycles where user-generated content is analyzed at regular intervals rather than continuously in real-time. This periodic approach maintains timely issue identification by processing content batches at optimized intervals, reducing computational complexity while preserving the ability to respond quickly to emerging topics.
4Measurement precision
If location-specific topic analysis is performed, then relevant local issues are identified accurately, but the system complexity increases due to multiple location contexts
Solution Approach 1:
The patent applies local quality by tailoring the analysis parameters and context weighting to each specific location. Instead of using a uniform analysis approach for all locations, the system adjusts contextual factors (social connections, local issues, geographic characteristics) to match each location's specific characteristics, achieving high accuracy without requiring a completely separate system for each location.
Data Source
AI summary
Performing semantic analysis on a user-generated text string includes training a neural network model with a plurality of known text strings to obtain a first distributed vector representation of the known text strings and a second distributed vector representation of a plurality of words in the known text strings, computing a relevance matrix of the first and second distributed representations based on a cosine distance between each of the plurality of words and the plurality of known text strings, and performing a latent dirichlet allocation (LDA) operation using the relevance matrix as an input to obtain a distribution of topics associated with the plurality of known text strings.