Sentiment Modules for Social Network Content Analysis
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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 sentiment categories (e.g., positive, negative, neutral) and further divides them into manageable modules such as sentiment summaries, top quotations, and mention clouds. This segmentation allows the system to process and present comprehensive sentiment information without overwhelming complexity.
Solution Approach 2:
The patent extracts key sentiment indicators from the full set of communications, such as dominant sentiment types, representative quotations, and frequently mentioned terms. By taking out only the most significant elements, the system maintains information completeness while reducing processing and presentation complexity.
2Measurement precision
If the system presents detailed analysis of all user communications, then the depth of sentiment understanding is improved, but the time required to process and display information increases
Solution Approach 1:
The patent performs preliminary sentiment analysis and categorization of communications before presentation. By pre-processing the data to identify dominant sentiments, key quotations, and important mentions, the system prepares the information in advance, enabling quick retrieval and display without sacrificing analytical depth.
Solution Approach 2:
The patent applies partial action by focusing analysis on the most relevant and impactful communications rather than processing every single communication in equal detail. The system identifies and emphasizes key sentiment drivers, providing deep understanding of the dominant sentiments while reducing overall processing time.
3Adaptability or versatility
If the system provides comprehensive modules for different types of information, then the versatility of the search-results interface is improved, but the complexity of the interface increases
Solution Approach 1:
The patent divides the comprehensive information presentation into separate, modular components including sentiment summaries, quotation modules, mention clouds, and trend indicators. Each module handles a specific type of information independently, allowing the system to provide versatile functionality while keeping each individual module simple and manageable.
Solution Approach 2:
The patent creates a universal module structure that can adapt to different types of content and sentiment data. The same modular framework handles various information types (sentiments, quotations, mentions) consistently, providing interface versatility without requiring separate complex handling for each information 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; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.