Sentiment Analysis Aggregation for Real-Time Financial Data
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Solution Overview
Problem
In information-intensive environments, such as the financial industry, users face challenges in obtaining a meaningful aggregate view of information from multiple sources, especially when data is received in real-time or pseudo-real-time and contains unstructured data like voice calls, due to the complexity and volume of information and variations in opinions from different sources.
Innovation Solution
A system that analyzes data from multiple sources, including active audio or video communications, by extracting keywords, analyzing contextual data, gauging sentiment, aggregating derived sentiment data, and presenting an aggregated view, using a combination of software, hardware, and network capabilities to process and display sentiment analysis in real-time or historic formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources including audio and video communications is collected in real-time, then the comprehensiveness of information coverage is improved, but the complexity and volume of unstructured data increases
Solution Approach 1:
The patent segments the data processing task by separating different data types (structured and unstructured) and processing them through different pathways. Audio and video communications are transcribed and processed separately from traditional structured data feeds, allowing the system to handle the volume increase without overwhelming complexity in a single processing pipeline.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes transcription services and sentiment analysis engines. These intermediaries convert unstructured audio/video data into structured text representations, which can then be aggregated with other structured data sources, thereby reducing the complexity of directly processing raw unstructured data from multiple sources.
2Speed
If real-time analysis of unstructured data from multiple sources is performed, then the timeliness of insights is improved, but the difficulty of obtaining a meaningful aggregate view increases
Solution Approach 1:
The patent extracts key information from unstructured audio and video data through transcription and sentiment analysis, separating the essential insights from the raw data. This extraction process creates a simplified representation that can be aggregated with other data sources, making it easier to obtain meaningful aggregate views while maintaining real-time processing capability.
Solution Approach 2:
The patent transforms unstructured audio/video data into structured text parameters through transcription, and then into sentiment parameters through analysis. This parameter transformation enables the data to be aggregated with other structured financial data sources, resolving the difficulty of creating meaningful aggregate views while maintaining real-time processing speed.
3Measurement precision
If sentiment analysis is performed on data from multiple sources, then the accuracy of aggregate sentiment view is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing sentiment analysis on specific keywords and phrases rather than analyzing every word in every communication. This selective analysis maintains adequate accuracy for financial sentiment monitoring while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The patent uses transcription services to create text copies of audio and video communications, which can then be processed more efficiently than the original unstructured data. This copying approach allows sentiment analysis to be performed on simplified text representations, reducing computational complexity while maintaining analysis accuracy.
Data Source
AI summary
Data is received from multiple data sources. At least one of the data sources is an active audio or video communication. The received data is analyzed by extracting instances of a keyword from the received data and analyzing contextual data near the keyword. Sentiment about the extracted keyword is gauged based on the contextual data. The derived sentiment data from the multiple data sources is aggregated, and an aggregated view of the derived sentiment data is presented.