Sentiment Analysis System for Privacy-Respecting Social Communities
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Solution Overview
Problem
Current techniques for generating social connections lack a system that can collect and interpret information without limitations while respecting privacy rights, particularly in promoting products and services or making personalized interactions.
Innovation Solution
A server-based system generates a transitory sentiment community by using a linguistic framework to analyze sentiment parameters, including a sarcasm sentiment module, and initiates a subscriber interface that mirrors the ebb and flow of the sentiment community, respecting individual privacy rights through real-time monitoring and threshold-based creation or deletion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a system collects and interprets information without limitations to promote products and services, then the effectiveness of product promotion and personalized interactions is improved, but privacy rights of individuals are violated
Solution Approach 1:
The system applies different levels of data collection and processing to different users based on their privacy preferences and consent levels. Some users receive full personalized interactions while others receive limited or no personalization, allowing the system to maintain effectiveness for consenting users while respecting privacy rights of non-consenting users.
Solution Approach 2:
The patent introduces an intermediary layer between raw data collection and personalized interaction generation. This intermediary processes information to extract only necessary sentiment parameters while filtering out personally identifiable information, enabling product promotion effectiveness without direct access to sensitive user data.
2Speed
If the system monitors sentiment parameters in real-time to generate transitory sentiment communities, then the responsiveness and personalization of interactions are improved, but the system complexity increases
Solution Approach 1:
The system segments the sentiment analysis process into distinct modular components: data collection module, sentiment parameter extraction module, community generation module, and interface initiation module. Each module handles a specific aspect of the process, making the overall complex system manageable and maintainable while achieving real-time responsiveness through parallel processing of these segments.
Solution Approach 2:
The system performs preliminary setup of sentiment parameter thresholds and community generation rules before real-time monitoring begins. This pre-configuration reduces the computational complexity during real-time operation, as the system only needs to evaluate incoming data against pre-established criteria rather than making complex decisions in real-time.
3Measurement precision
If the system creates transitory sentiment communities based on threshold sentiment parameters, then the accuracy of sentiment-based interactions is improved, but the loss of information occurs due to threshold-based filtering
Solution Approach 1:
The system applies threshold-based filtering as a partial action rather than a complete filter. Instead of discarding data that falls below thresholds, the system uses thresholds to prioritize and focus analysis on high-confidence sentiment cases while still collecting and storing all raw data for potential future analysis, thus maintaining measurement precision for critical cases without complete information loss.
Solution Approach 2:
The system creates copies of sentiment data at different processing stages. Threshold-based filtering operates on copied data representations while the original comprehensive data is preserved. This allows the system to achieve accurate sentiment-based community generation from filtered data while retaining access to complete information for verification or alternative analysis approaches.
Data Source
AI summary
A method and system of initiating a subscriber interface at a client subscriber device coupled to a server device. The method comprises receiving, at a memory of the server computing device, a specification identifying at least one sentiment parameter in association with the subscriber computing device, the at least one sentiment parameter being related to a sarcasm sentiment, identifying, within social media content data received at the server computing device, content associated with a subject of interest, the subject of interest defined in accordance with at least one text character string, modifying, upon detecting the sarcasm sentiment within the content associated with the subject of interest as being one of above and below a sarcasm sentiment likelihood threshold, a sentiment classification associated with the at least one sentiment parameter and transmitting, to the subscriber computing device, a notification of the at least one sentiment parameter and the modified sentiment classification.