Multi-dimensional Query Extraction for Polarity-Aware Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search systems fail to consider context and polarity when recommending content, leading to irrelevant results as they do not distinguish between documents with different sentiments and contexts, even when searching for specific topics like 'viral marketing' where efficiency is a critical factor.
Innovation Solution
A multi-dimensional query method that takes a query tuple comprising keywords, polarity, and a polarity score, allowing for context-aware searches by extracting and ranking documents based on their relevance to both the query and sentiment, using natural language processing to analyze and output polarity-aware content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search systems are used to search for documents by keyword, then the search system is simple and fast, but the search results do not consider context and polarity leading to irrelevant recommendations
Solution Approach 1:
The patent extends the traditional keyword search by adding two new dimensions: polarity (positive/negative sentiment) and context (document topic/category). This transforms a one-dimensional keyword match into a multi-dimensional filtering process, allowing the system to distinguish between documents with the same keywords but different sentiments and contexts, thereby improving search relevance without overwhelming complexity
Solution Approach 2:
The search system is segmented into independent processing stages: keyword extraction, polarity detection, and context analysis. Each stage processes specific aspects of the document independently and the results are combined through filtering. This segmentation allows the system to handle complex multi-dimensional search requirements through modular processing, improving precision while managing complexity
2Measurement precision
If all documents with matching keywords are returned, then the search system is simple, but documents with different sentiments and contexts are mixed together making results irrelevant
Solution Approach 1:
The system applies different processing qualities to different aspects of documents locally. Keyword matching uses standard text processing, while polarity and context analysis use specialized NLP models. This allows the system to maintain simplicity for basic search while adding sophisticated analysis only where needed, distinguishing between positive and negative polarity documents without processing every document uniformly at high complexity
3Measurement precision
If polarity and context analysis is added to search results, then content recommendation becomes relevant, but the processing time and computational resources increase
Solution Approach 1:
The system performs polarity detection and context analysis in advance during the indexing phase, before actual search queries are received. Document metadata is pre-enriched with polarity labels and context tags, transforming the search operation from a complex real-time analysis to a simpler query-against-pre-processed-data operation. This preliminary action significantly reduces search processing time while maintaining high accuracy
Solution Approach 2:
The patent extracts only the essential polarity and context information from full documents, storing these as metadata rather than processing complete document contents during search. This extraction approach allows the system to maintain comprehensive search capabilities while reducing computational resources and processing time during actual query execution
Data Source
AI summary
A method of multi-dimensional query based extraction of polarity-aware content from a plurality of documents. An input query tuple comprising: at least one keyword, polarity, and quantification of polarity corresponding to a polarity score is received from a user via an interface. A search of metadata of indexed documents based on the input query tuple is conducted to identify documents which satisfy at least some of the input query tuple. For each document which satisfies at least some of the input query tuple from the user, keywords, polarity associated with the keywords, and excerpts of each of the documents including the keywords and text containing polarity context associated with the keywords are extracted and for each document which satisfies at least some of the input of the input query tuple, the query results to the user via the user interface are outputted.