Trending Topic Mining via Deduplication and Scoring
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Solution Overview
Problem
Current methods for identifying trending topics in a text corpus face challenges such as noise, duplication, high computational cost, and non-intuitive results due to the vast amount of unstructured data from various sources, including online forums and social media, which complicates the analysis of emerging sentiments and viral topics.
Innovation Solution
A system and method that analyzes a document corpus to identify topics, removes duplicates, calculates a trending score for each topic based on frequency across time periods, and ranks them for display, utilizing techniques like Latent Dirichlet Allocation and filtering to surface the most relevant topics efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical methods like word frequency counting or LDA are used to identify trending topics, then topic identification can be performed, but noise and false positives increase significantly
Solution Approach 1:
The patent introduces an intermediary deduplication layer between topic identification and result output. This layer uses similarity comparison (cosine similarity calculation) to identify and merge duplicate topics, acting as a mediator that filters out redundant information while preserving meaningful variations. The intermediary process transforms the raw topic list into a deduplicated set, resolving the contradiction by adding a filtering mechanism that improves reliability without eliminating the productive statistical analysis methods.
2Loss of information
If advanced statistical methods like LDA are used to analyze topics, then deeper insights can be obtained, but computational cost increases significantly
Solution Approach 1:
The patent applies partial action by implementing selective deduplication only for topics that exceed a similarity threshold, rather than processing all topics uniformly. The system calculates cosine similarity only when needed and applies deduplication selectively to reduce computational overhead. This partial processing approach maintains the information extraction depth of advanced methods like LDA while reducing the excessive computational cost by avoiding unnecessary processing of already-unique topics.
3Productivity
If comprehensive topic analysis is performed on all identified topics, then complete coverage is achieved, but duplication and redundancy increase
Solution Approach 1:
The patent extracts and removes duplicate topics from the comprehensive topic set through a dedicated deduplication process. The system identifies topics that are semantically similar (above a configurable similarity threshold) and removes redundant instances, keeping only the most representative topic. This extraction approach maintains complete coverage of unique topics while eliminating duplication, resolving the contradiction between comprehensive analysis and redundancy.
4Loss of information
If all identified topics are displayed to users, then complete information is provided, but results become non-intuitive and difficult to interpret
Solution Approach 1:
The patent implements feedback through the trending score calculation mechanism. The system calculates trending scores for all topics based on multiple factors (topic frequency, recency, document count), then uses this feedback to rank and prioritize topic display. This feedback loop transforms the complete but unordered topic set into a ranked list that maintains information completeness while improving interpretability through scoring-based prioritization.
Data Source
AI summary
A system and method for identifying trending topics in a document corpus are provided. First, multiple topics are identified, some of which topics may be filtered or removed based on co-occurrence. Then, for each remaining topic, a frequency of the topic in the document corpus is determined, one or more frequencies of the topic in one or more other document corpora are determined, a trending score of the topic is generated based on the determined frequencies. Lastly, the remaining topics are ranked based on the generated trending scores.