Synonymous Topic Merging via Attribute Similarity
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Solution Overview
Problem
Existing topic mining and sentiment analysis techniques face challenges such as computational inefficiency, production of overlapping topics, requirement of significant training data, and difficulty in adapting to new or noisy data sets, particularly in processing large sets of unstructured data from diverse sources like social media.
Innovation Solution
A system that combines topic extraction and sentiment analysis by segmenting content items into clauses using connective words and punctuation, generating topics as n-grams, and applying filters to exclude high-frequency words and morphological filters, while using statistical models for sentiment classification and attribute discovery, and merging synonymous topics based on attribute overlap and morphological similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If metrics such as tf-idf are used to identify potential topics, then computational efficiency is improved, but a large number of topics with significant overlap are produced
Solution Approach 1:
The patent changes the parameters used for topic identification from traditional tf-idf metrics to a hybrid approach combining tf-idf with semantic similarity metrics and attribute-based filtering. This allows the system to maintain computational efficiency while reducing topic overlap by filtering out semantically redundant topics through parameter adjustments in the scoring and selection process.
Solution Approach 2:
The patent introduces an intermediary filtering layer between topic extraction and topic selection. This intermediary component uses semantic similarity calculations and attribute overlap analysis to evaluate candidate topics and eliminate redundant ones before final topic determination, thereby reducing overlap while preserving computational efficiency.
2Measurement precision
If statistical topic models are used to extract topics, then topic quality is improved, but significant amounts of training data and computational overhead are required
Solution Approach 1:
The patent segments the topic extraction process into multiple independent stages: initial topic candidate generation using lightweight methods, semantic attribute extraction, similarity-based filtering, and final topic selection. This segmentation allows the system to achieve high topic quality through progressive refinement without requiring the heavy computational overhead of end-to-end statistical topic models.
Solution Approach 2:
The patent applies partial action by using simplified topic extraction methods for initial candidate generation, then applying more sophisticated semantic analysis only to the most promising candidates. This approach achieves high topic accuracy for the final output while avoiding the computational expense of applying complex models to all possible topic candidates.
3Ease of operation
If conventional sentiment analysis techniques are applied to documents with multiple sentiments, then overall sentiment assignment is simplified, but accuracy in capturing nuanced sentiments is reduced
Solution Approach 1:
The patent segments sentiment analysis at the clause level rather than applying a single overall sentiment label to entire documents. Each clause containing a topic is analyzed independently for sentiment, allowing the system to capture multiple sentiments within a single document while maintaining operational simplicity through automated clause-level processing.
Solution Approach 2:
The patent applies local quality by assigning sentiment labels to specific clauses and topics rather than uniformly to entire documents. This allows different parts of a document to have different sentiment characteristics, capturing nuanced sentiments accurately while maintaining ease of operation through automated local sentiment assignment.
4Ease of manufacture
If sentiment analysis systems rely on structured data sets, then model training is simplified, but adaptability to new domains and noisy data sets is reduced
Solution Approach 1:
The patent creates a universal sentiment analysis framework that can process both structured and unstructured data from multiple domains. The system uses domain-agnostic feature extraction and sentiment classification methods that can be applied across different data types and domains, achieving high adaptability while maintaining training simplicity through transferable learning approaches.
Solution Approach 2:
The patent adjusts parameters and thresholds dynamically based on the characteristics of the input data and domain. This allows the system to adapt to new domains and noisy datasets by changing processing parameters rather than requiring complete retraining, maintaining ease of manufacture while improving versatility.
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of topics associated with a set of content items. Next, the system obtains a first set of attributes associated with a first topic in the set of topics and a second set of attributes associated with a second topic in the set of topics. Next, the system calculates a similarity between the first and second sets of attributes and applies a threshold to the similarity to identify the first and second topics as synonymous when the similarity exceeds a threshold. The system then merges the first and second topics under a representative topic. Finally, the system displays the representative topic to a user to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.