Quotations-Module for Social Network Sentiment Aggregation
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Solution Overview
Problem
Social networking systems lack effective methods to aggregate and present user sentiments and reactions to content items, such as news stories or videos, in a concise and accessible manner, limiting users' ability to understand collective emotions and opinions.
Innovation Solution
The system generates customized search-results interfaces that include modules like sentiments-modules, quotations-modules, and mentions-modules, which analyze and visualize user communications to provide sentiment scores, popular quotations, and frequently mentioned terms, allowing users to gauge collective emotions and reactions to content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system aggregates all user communications about a content item, then the completeness of sentiment information is improved, but the complexity of data processing and presentation increases
Solution Approach 1:
The patent segments the large volume of user communications into distinct modules (sentiments-module, quotations-module, mentions-module) that each handle specific types of information. This segmentation allows the system to process and present different aspects of user reactions separately, reducing overall complexity while maintaining information completeness.
Solution Approach 2:
The system extracts key elements (sentiments, quotations, mentions) from the full set of user communications and presents them in dedicated modules. This extraction approach maintains the essential information while simplifying presentation by focusing on the most relevant aspects rather than displaying all raw communications.
2Measurement precision
If the system provides detailed analysis of all user communications, then the precision of sentiment analysis is improved, but the time required to process and display information increases
Solution Approach 1:
The system performs partial analysis by focusing on extracting specific elements (sentiments, quotations, mentions) rather than analyzing every aspect of all communications in detail. This partial action approach provides sufficient precision for understanding user reactions while significantly reducing processing time compared to comprehensive analysis of all communications.
Solution Approach 2:
The patent employs automated natural language processing and text analysis algorithms to rapidly analyze communications, replacing manual analysis methods. This substitution enables precise sentiment analysis to be performed automatically and quickly, maintaining measurement precision while eliminating time losses associated with manual processing.
3Loss of information
If the system displays comprehensive information from user communications, then the usefulness of the interface is improved, but the accessibility and ease of use decreases
Solution Approach 1:
The interface is segmented into distinct modules (sentiments-module, quotations-module, mentions-module) that organize comprehensive information in structured, easily navigable sections. This segmentation makes the abundant information accessible and easy to use by allowing users to quickly locate specific types of content without being overwhelmed by a monolithic display.
Solution Approach 2:
Each module is designed with specific local qualities tailored to its function - the sentiments-module presents emotional tones, the quotations-module highlights key phrases, and the mentions-module displays frequently referenced terms. This local quality optimization ensures that each type of information is presented in the most accessible and useful format for that specific content type.
Data Source
AI summary
In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, quotations from the text of the communication; determining, for each extracted quotation, partitions of the quotation; grouping the extracted quotations into clusters based on a respective degree of similarity among their respective partitions; calculating a cluster-score for each cluster based on a frequency of occurrence of partitions of quotations in the cluster in the communications; and generating a quotations-module comprising representative quotations, each representative quotation being a quotation from a cluster having a cluster-score greater than a threshold cluster-score.