Snippet-Based Social Media Text Extraction and Ranking
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Solution Overview
Problem
Current search methods for social media documents are inefficient due to their unstructured, noisy, and verbose nature, often returning entire documents when only a small portion is relevant, making effective monitoring and analysis challenging.
Innovation Solution
The method indexes social media documents into snippets offline, allowing for efficient keyword matching and ranking based on purpose, using a semantic query expansion module to identify related keywords and a purpose-driven snippet ranker to provide relevant snippets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current search methods analyze all documents on the fly, then they can process complete social media documents, but the processing time increases and efficiency decreases
Solution Approach 1:
The patent divides social media documents into smaller units called snippets (e.g., sentences or paragraphs) for indexing and processing. This segmentation allows the system to process only relevant portions of documents rather than entire documents, significantly reducing processing time while maintaining search result relevance through the snippet-based approach
Solution Approach 2:
The patent performs preliminary indexing of documents into snippets before actual search queries are executed. By pre-processing and organizing documents into indexed snippets with metadata, the system can quickly retrieve and rank relevant snippets during search without analyzing complete documents on the fly, thus reducing query processing time
2Quantity of substance
If search algorithms return entire documents, then they provide complete information, but the quantity of relevant information decreases when only small portions are relevant
Solution Approach 1:
The patent extracts and returns only the relevant snippets from social media documents rather than entire documents. By identifying and extracting specific snippets that match search queries based on keyword matching and ranking algorithms, the system provides complete relevant information while filtering out irrelevant portions, thus improving relevance accuracy
Solution Approach 2:
The patent applies different processing and ranking qualities to different portions of documents. By analyzing and ranking snippets individually based on their relevance to the query, the system can provide localized quality improvement where each snippet is evaluated on its own merit rather than treating the entire document uniformly, thereby improving relevance accuracy
3Loss of information
If the system processes long social media documents, then it captures complete context, but the complexity of analysis increases
Solution Approach 1:
The patent segments long social media documents into smaller snippets that can be processed independently. This segmentation reduces the complexity of analysis by breaking down complex documents into manageable units while preserving contextual information through the snippet-based approach, allowing the system to maintain context completeness without overwhelming complexity
4Productivity
If the system indexes all documents into snippets, then the search efficiency improves, but the storage requirements increase
Solution Approach 1:
The patent extracts only the necessary information from documents by indexing snippets rather than entire documents. By storing only relevant snippet portions along with their metadata and position information, the system achieves improved search efficiency while minimizing storage requirements compared to storing complete documents
Solution Approach 2:
The patent applies partial indexing by storing only the necessary snippet information rather than complete document content. This partial action approach balances storage requirements with search efficiency, indexing only the portions of documents that are needed for effective search queries
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus for extracting text from a social media document are disclosed. For example, the method indexes a plurality of social media documents into a plurality of snippets, receives a query including one or more keywords and a purpose, identifies one or more of the plurality of snippets that include the one or more keywords in an index, ranks the one or more of the plurality of snippets in accordance with the purpose and provides the one or more plurality of snippets that are ranked in accordance with the purpose.