NLP Joint Topic-Sentiment Detection via NMF
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Solution Overview
Problem
Existing natural language processing (NLP) systems face challenges in accurately and efficiently detecting reliable properties for relatively short NLP input data due to limited feature extraction capabilities, often relying on computationally inefficient methods and failing to provide reliable sentiment analysis, especially in healthcare feedback processing.
Innovation Solution
The use of non-negative matrix factorization (NMF) and non-negative matrix tri-factorization (3-factor NMF) models to generate term-topic and document-sentiment correlation data, integrating domain-specific data for efficient and accurate detection of semantic properties in short NLP input data, such as healthcare feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP methods are used for short input data, then the system is simpler to implement, but the detection accuracy and reliability deteriorate due to limited feature extraction
Solution Approach 1:
The patent segments the NLP processing into distinct modules: feature extraction module, sentiment detection module, and topic detection module. Each module handles specific aspects of the analysis independently, allowing for improved accuracy through specialized processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent extends traditional NLP by adding sentiment and topic dimensions to the analysis. Instead of only analyzing textual features, the system incorporates sentiment scores and topic classifications, creating a multi-dimensional feature space that significantly improves detection accuracy for short input data.
2Reliability
If advanced feature extraction methods are used to improve detection accuracy, then the reliability improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary feature extraction and correlation computation on training data before actual detection. The system pre-computes term-document correlations and builds sentiment-topic models in advance, so that during actual short input processing, the system can quickly query pre-computed features rather than performing complex computations in real-time.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on input characteristics. For short inputs, the system optimizes by focusing on the most relevant pre-computed features and adjusting the depth of analysis, thereby maintaining high reliability while reducing computational overhead for efficient processing.
3Measurement precision
If more feature data is extracted from short NLP input, then the detection reliability improves, but the computational resources and time required increase
Solution Approach 1:
The patent extracts only the most relevant features from short NLP input data using pre-computed term-document correlations and sentiment-topic models. Instead of analyzing all possible features, the system selectively extracts high-value features that have been identified as important during the training phase, thereby improving detection quality while minimizing processing time.
4Reliability
If joint topic-sentiment detection is implemented, then the overall detection accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent merges topic detection and sentiment detection into a unified joint detection framework. By combining these functions, the system achieves improved overall accuracy as both detections benefit from shared feature extraction and mutual reinforcement, while the modular implementation keeps system complexity manageable.
Solution Approach 2:
The patent creates a multi-functional system where the same feature extraction infrastructure serves both topic detection and sentiment detection. The sentiment-topic models and term-document correlations are universally applied across both detection tasks, reducing redundant computation and managing system complexity through shared components.
Data Source
AI summary
There is a need for solutions for more effective and efficient natural language processing systems. This need can be addressed, for example, by a system configured to obtain a term correlation data object for a plurality of digital documents; determine, based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents; generate the topic detection based at least in part on the document-topic correlation object; and generate the sentiment detection based at least in part on the document-sentiment correlation object.