Ambiguity-First Sentiment Analysis for Faster Information Filtering
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Solution Overview
Problem
The increasing volume and ambiguity of information on the Internet make it difficult for users to obtain filtered information related to a specific meaning or sentiment, hindering quick decision-making.
Innovation Solution
A method and system for sentiment analysis using an ambiguity analysis model and a sentiment analysis model, involving information acquisition, lexicon construction, and model training to identify and analyze non-ambiguous information with sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sentiment analysis is performed on all internet information, then comprehensive sentiment coverage is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing ambiguity analysis before sentiment analysis. The system pre-processes information to identify and filter ambiguous content, creating a refined dataset that is then subjected to sentiment analysis. This sequential approach prevents wasted computational resources on ambiguous information that cannot be reliably analyzed for sentiment, thereby improving efficiency while maintaining accuracy.
2Measurement precision
If ambiguity analysis is performed to filter information, then information precision is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the information analysis process into two distinct modules: an ambiguity analysis module and a sentiment analysis module. Each module has a specific function - the first identifies ambiguous information, and the second analyzes sentiment of filtered content. This segmentation allows the system to handle complex tasks through specialized, manageable components rather than a single monolithic system.
Solution Approach 2:
The patent uses an intermediary approach by introducing an ambiguity analysis module as a mediator between raw information and sentiment analysis. This intermediate layer filters out ambiguous content before it reaches the sentiment analysis engine, ensuring that only clear, unambiguous information is processed for sentiment. This mediator improves overall system precision while keeping each individual module relatively simple.
3Reliability
If filtered non-ambiguous information is extracted, then decision-making quality is improved, but information loss increases
Solution Approach 1:
The patent applies local quality by applying different processing treatments to different types of information based on their ambiguity characteristics. Rather than uniformly processing all information, the system identifies ambiguous regions and excludes them from sentiment analysis, while thoroughly analyzing non-ambiguous regions. This selective approach ensures high-quality analysis where applicable while minimizing unnecessary processing of problematic content.
Data Source
AI summary
One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.