Mentions-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 in a comprehensive and accessible manner, making it difficult for users to gauge collective opinions and emotions related to trending content.
Innovation Solution
The system generates customized search-results interfaces that include modules such as sentiments-modules, quotations-modules, and mentions-modules, which analyze and visualize user communications to provide insights into sentiments, popular quotations, and commonly used terms associated with content items, allowing users to understand collective emotions and reactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system aggregates all user communications related to 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 user communications into distinct categories through specialized modules: sentiments-module for emotional analysis, quotations-module for notable statements, and mentions-module for referenced entities. This segmentation allows the system to process and present comprehensive sentiment information while managing complexity through modular organization of data types and processing logic.
Solution Approach 2:
The patent introduces intermediary processing layers between raw communications and user presentation. Sentiment analysis engines, quotation extraction algorithms, and mention detection systems act as intermediaries that transform unstructured communications into structured, analyzable data formats, reducing the complexity of directly processing all raw user inputs.
2Loss of information
If the system provides detailed analysis of user communications, then the depth of insights is improved, but the time required to generate results increases
Solution Approach 1:
The patent implements partial action by applying different levels of analysis depth to different communication types. The sentiments-module performs emotional analysis, the quotations-module extracts notable statements, and the mentions-module identifies referenced entities. This selective, partial analysis approach provides deep insights for each category without requiring exhaustive analysis of all communication aspects, thereby reducing overall processing time while maintaining insight depth.
3Loss of information
If the system presents multiple types of communication information, then the comprehensiveness of search results is improved, but the user interface complexity increases
Solution Approach 1:
The patent segments comprehensive communication information into distinct, visually separated modules on the user interface. Each module (sentiments, quotations, mentions) presents specific types of information in dedicated sections, allowing the system to provide comprehensive search results while maintaining interface simplicity through clear spatial organization and modular presentation of different information types.
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, n-grams from the text of the communication; identifying mention-terms from the extracted n-grams, each mention-term being a noun-phrase; calculating a term-score for each mention-term based on a frequency of occurrence of the mention-term in the communications; and generating a mentions-module including mentions, each mention including a mention-term having a term-score greater than a threshold term-score and text from communications comprising the mention-term.