Sentiment-Aware Topic Extraction Using Gibbs Sampling
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Solution Overview
Problem
Conventional methods are unable to automatically extract topics that include sentiment-oriented ratable aspects and sentiments from documents, particularly in social network and blog posts, where only opinions and sentiments are described without explicit ratings.
Innovation Solution
An apparatus and method that calculates probability distributions for sentiment global and local topics, performs statistical inference using Gibbs sampling, and extracts relevant topics and sentiments from documents, enabling the automatic extraction of sentiment-oriented ratable aspects and sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional topic extraction methods are used, then topic extraction can be performed, but sentiment-oriented ratable aspects and sentiments cannot be extracted
Solution Approach 1:
The patent segments the topic extraction process into multiple specialized components: global topic extraction, local topic extraction, and sentiment extraction. Each component focuses on specific aspects of the document, allowing the system to extract both traditional topics and sentiment-oriented ratable aspects with high precision while maintaining versatility
Solution Approach 2:
The patent creates a multi-functional topic extraction system that can simultaneously extract global topics, local topics, and sentiment information from the same document corpus. This universal approach enables the system to handle diverse extraction requirements without needing separate specialized systems
2Productivity
If LDA and EM algorithm are used, then topic extraction can be performed, but the method cannot extract sentiment information
Solution Approach 1:
The patent merges the efficiency of LDA-EM algorithm with sentiment extraction capabilities by integrating sentiment analysis into the topic modeling framework. The system combines global topic distribution extraction with local sentiment aspect extraction, maintaining computational efficiency while preventing loss of sentiment information through coordinated inference
3Device complexity
If global topic extraction only is performed, then computation is simplified, but local sentiment aspects are missed
Solution Approach 1:
The patent segments topic extraction into global and local levels, where global topics provide overall document themes and local topics capture specific sentiment aspects. This segmentation allows the system to manage computation complexity through hierarchical processing while achieving high precision in sentiment extraction by focusing computational resources on relevant local aspects
Data Source
AI summary
In accordance with a first exemplary embodiment, there is provided a method for extracting semantic topics from document sets in which opinions about an object are described using an apparatus capable of calculating a probability distribution. The method include (a) extracting word distributions about sentiment global topics and sentiment local topics; (b) extracting a global topic distribution, a local topic distribution and sentiment distributions about the global and local topics from the document sets; (c) performing statistical inference about each of the distributions extracted in the step (a) and the step (b); (d) extracting a global or local topic and a sentiment relevant to the global or local topic from the distributions of the inference performed in the step (c); and (e) extracting a word from the word distributions about sentiment global topics or sentiment local topics on the basis of the topic and sentiment extracted in the step (d).