Contextual Linking in Time-Dependent Media for Sentiment Analysis
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Solution Overview
Problem
Text mining techniques fail to accurately extract information from time-dependent media due to the lack of contextual information such as intonation and facial expressions, leading to incomplete or inaccurate results in sentiment analysis and search queries.
Innovation Solution
A system that extracts text and non-text features from time-dependent media, maps them based on temporal positions, and supplements text with contextual information by identifying patterns in non-text features like intonation and facial expressions, linking them to context elements, and modifying the content accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text mining is performed on time-dependent media without contextual information, then the processing speed is fast, but the accuracy of sentiment analysis and information extraction is low
Solution Approach 1:
The TDM content is divided into multiple documents based on temporal positions and contextual boundaries. This segmentation allows the system to process manageable chunks of content while preserving temporal relationships, enabling accurate sentiment analysis without overwhelming system complexity
Solution Approach 2:
Context elements serve as intermediaries that link non-text features (intonation, facial expressions) with text features. These context elements capture contextual information and facilitate accurate interpretation of text in time-dependent media without requiring direct complex analysis of all raw features
2Measurement precision
If all non-text features are analyzed for pattern matching, then the contextual accuracy is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system extracts only the relevant non-text features (intonation patterns, facial expressions) that are temporally aligned with text features. By selectively extracting and analyzing only the necessary contextual features rather than all possible features, the system maintains high contextual accuracy while reducing computational overhead and processing time
Solution Approach 2:
Context elements are pre-defined and prepared before pattern matching occurs. This preliminary preparation of context patterns allows for faster matching during actual processing, as the system doesn't need to create and analyze all possible context combinations in real-time
Data Source
AI summary
Time dependent media (TDM) content is received, and text features and non-text features are extracted from the TDM content. The TDM content is split into two or more documents. A document, which includes non-text features from the extracted non-text features, is selected. Non-text features in the document are compared to context patterns. When a context pattern matches a non-text feature in the document, a context element linked to the context pattern is linked to the non-text feature as well. The TDM content is modified based on the context element.