Server Sentiment Analysis System for Sarcasm Detection
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Solution Overview
Problem
Current techniques for associating social connections of users lack a system that can effectively collect and interpret information without limitations, while respecting privacy rights and failing to account for nuances like sarcasm in sentiment analysis.
Innovation Solution
A server-based system that generates a transitory sentiment community by receiving data from multiple sources, preprocessing it, performing sentiment analysis using a training model, and modifying sentiment intensity ratings to account for sarcasm, all while respecting privacy and focusing on real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current techniques associate social connections of users for promoting products and services, then personalized interactions can be achieved, but the system lacks ability to collect and interpret information without limitations while respecting privacy rights
Solution Approach 1:
The patent segments the information collection process into distinct modules: data collection module, sentiment analysis module, sarcasm detection module, and community generation module. Each module handles specific tasks independently, allowing the system to collect and interpret information from multiple sources without requiring a single complex system, thereby resolving the contradiction between information collection capability and system complexity.
Solution Approach 2:
The patent introduces an intermediary sentiment analysis system that processes raw data from multiple sources before generating insights. This intermediary layer filters, analyzes, and interprets information while maintaining privacy boundaries, enabling versatile information collection without directly exposing the complexity of the underlying system architecture.
2Measurement precision
If literal interpretations of collected information are used, then processing is simple, but nuances like sarcasm in sentiment analysis are not captured
Solution Approach 1:
The patent applies preliminary action by pre-training sentiment analysis models and sarcasm detection algorithms before actual data processing. The system pre-processes collected information through multiple analysis layers, including sentiment classification and sarcasm detection, before generating final insights. This preliminary processing ensures nuanced interpretation without requiring complex real-time analysis, resolving the contradiction between accuracy and process complexity.
Solution Approach 2:
The patent replaces simple literal interpretation mechanisms with advanced natural language processing and machine learning models. These computational models automatically detect sarcasm, sentiment, and nuanced meanings in text data, achieving high measurement precision without manually complex analysis processes. The substitution of mechanical interpretation with intelligent algorithms resolves the contradiction between accuracy and complexity.
3Productivity
If transitory sentiment communities are monitored in real-time, then actionable insights are provided, but computational resources and processing time increase
Solution Approach 1:
The patent implements periodic action by monitoring transitory sentiment communities at scheduled intervals rather than continuously. The system periodically collects data, performs sentiment analysis, and generates insights at optimized frequencies based on community activity levels. This periodic monitoring provides timely actionable insights while significantly reducing computational resource consumption compared to continuous real-time processing, resolving the contradiction between productivity and energy use.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting monitoring thresholds, analysis depth, and processing frequency based on community sentiment intensity and activity levels. When sentiment fluctuations exceed predefined thresholds, the system increases monitoring intensity; otherwise, it reduces processing resources. This adaptive parameter adjustment enables fast insight generation when needed while conserving computational resources during stable periods, resolving the contradiction between productivity and energy consumption.
Data Source
AI summary
A method and system, performed in a processor of a server computing device, of engaging a transitory sentiment community. The method comprises identifying, responsive to monitoring generation of the transitory sentiment community, a critical engagement juncture being reached, the transitory sentiment community including a collective of content consumers, the collective of content consumers providing a sentiment expressive usage associated with a subject of interest, the sentiment expressive usage characterized in accordance with a sentiment intensity rating, and initiating, responsive to the critical engagement juncture being reached, an engagement action directed to at least a subset of the collective of content consumers.