Data Mining Method for Public Opinion Analysis
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Solution Overview
Problem
Current data mining methods for public opinion analysis are inefficient and inaccurate in extracting relevant data from massive Internet data, particularly in categorizing low quality data, low quality content, and inaccurate sentiment analysis.
Innovation Solution
A data mining method that employs multiple data identification strategies, including rule matching, keyword extraction, and sentiment polarity analysis, to classify data into low quality data, low quality content, and inaccurate sentiment analysis, using a processor-based system and storage medium to execute these strategies efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data mining methods are used to process massive Internet data, then the processing can be performed with simple systems, but the efficiency and accuracy of extracting relevant public opinion data is low
Solution Approach 1:
The patent segments the data mining process into multiple independent modules: data acquisition module, data cleaning module, data classification module, and data analysis module. Each module handles specific tasks (e.g., removing ads and spam, categorizing into low quality data/low quality content/inaccurate sentiment analysis), allowing parallel processing and improving overall efficiency without requiring a monolithic complex system.
Solution Approach 2:
The patent introduces intermediate processing layers including data cleaning as an intermediary between raw data acquisition and classification, and separate classification strategies as intermediaries between cleaning and final analysis. These intermediary layers filter and prepare data progressively, enhancing accuracy while maintaining modular system architecture.
2Measurement precision
If multiple data identification strategies are implemented to improve data classification accuracy, then data quality improves, but the complexity of the mining system increases
Solution Approach 1:
The patent divides data identification into three distinct strategies: rule matching for low quality data identification, keyword extraction for low quality content identification, and sentiment polarity analysis for inaccurate sentiment analysis. Each strategy targets specific data quality issues independently, improving classification accuracy while maintaining clear separation of functions to manage complexity.
Solution Approach 2:
Different identification strategies are applied locally to different data quality dimensions: rule matching specifically targets structural low quality data, keyword extraction targets content quality, and sentiment analysis targets emotional accuracy. This localized application of specialized methods improves precision for each category without requiring all methods to be applied everywhere, managing system complexity.
3Productivity
If manual data mining methods are used, then the system remains simple, but labor costs are high and processing speed is slow
Solution Approach 1:
The patent implements automated self-service mechanisms where the system automatically acquires data from multiple sources, performs cleaning by identifying and removing ads/spam, classifies data using predefined rules and algorithms, and generates analysis results without human intervention. This automation dramatically increases processing speed while the modular design maintains operational simplicity through clear workflow separation.
Solution Approach 2:
The patent replaces manual mechanical data processing operations with automated computational methods: rule-based automated filtering replaces manual data sorting, keyword extraction algorithms replace manual content review, and sentiment polarity algorithms replace manual emotional analysis. This substitution increases processing speed while the structured automated system remains easier to operate than manual processes.
Data Source
AI summary
The present disclosure discloses a data mining method, a data mining apparatus, an electronic device and a storage medium, relates to the technical field of the Internet, and in particular to the Internet big data processing technology. The method includes: acquiring a current article to be mined; obtaining information values required for each data identification strategy of multiple data identification strategies from the current article, and each data identification strategy is used for identifying a preset type of data; identifying a data type of the current article according to the information values required for each data identification strategy to obtain a data type identification result; and determining whether the current article belongs to any preset type of data according to the data type identification result.