Sentiment Data Generation for Search Relevance
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Solution Overview
Problem
Conventional search engines are inefficient in determining sentiment associated with specific topics, as they return numerous irrelevant results, making it difficult for users to find sentiment data within search queries.
Innovation Solution
A method and system for automatically generating sentiment data by analyzing portions of documents to determine sentiment scores and categories, allowing for the storage and use of this data in filtering and ranking search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional search engines use keywords to locate webpages, then the search engine can return webpages containing the search terms, but the user must wade through a large number of irrelevant search results to find sentiment information
Solution Approach 1:
The patent segments the search result processing by introducing sentiment analysis that divides results into sentiment categories (positive, negative, neutral) and identifies sentiment-bearing portions within documents. This segmentation allows users to quickly locate relevant sentiment information without examining all search results, resolving the contradiction between information retrieval efficiency and volume of results.
2Measurement precision
If conventional search engines return all webpages containing search keywords, then comprehensive results are provided, but it is difficult or impossible to determine sentiment without manually reviewing each result
Solution Approach 1:
The patent applies preliminary action by performing sentiment analysis on search results before presenting them to users. The system pre-processes documents to identify sentiment-bearing portions, determine sentiment categories, and calculate sentiment scores. This preliminary sentiment determination eliminates the need for users to manually review each result, thereby reducing time loss while maintaining accurate sentiment measurement.
3Productivity
If sentiment analysis is performed on search results, then sentiment data can be efficiently determined, but additional processing steps are required beyond conventional keyword matching
Solution Approach 1:
The patent applies universality by designing a sentiment analysis system that performs multiple functions: keyword matching, sentiment portion identification, sentiment category classification, and sentiment score calculation. This multi-functional approach integrates sentiment analysis into the existing search framework, improving productivity without requiring entirely separate processing systems, thereby managing complexity while enhancing efficiency.
Data Source
AI summary
A method, computer-readable medium, and a computer system for automatically generating sentiment data are disclosed. One or more portions of at least one document may be determined to be associated with at least one sentiment of one or more other portions of the at least one document. One or more scores associated with the at least one sentiment may be automatically determined based on at least one respective attribute of the one or more portions. The at least one respective attribute may include a positive category, a negative category, a neutral category, a degree associated with a positive sentiment, a degree associated with a negative sentiment, some combination thereof, etc. In this manner, data associated with sentiment of one or more portions of at least one document may be generated.