Forwarded Media Comment Filtering via Sentiment Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social media platforms face challenges in delivering only relevant comments to users, as forwarded media content often includes comments from various sources, making it difficult to personalize the delivery based on user interests.
Innovation Solution
A system and method utilizing artificial intelligence and IoT technologies to manage aggregated comments by identifying user preferences and classifying comments based on sentiment and relevance, using a neural network model to extract and prioritize comments from knowledgeable sources aligned with user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all comments from forwarded media content are delivered to users, then complete information is provided, but network efficiency deteriorates due to transmission of irrelevant data
Solution Approach 1:
The system extracts only the relevant comments from the aggregated comments of forwarded media content based on user preferences and sentiment analysis, rather than transmitting all comments. This extraction process removes irrelevant information before transmission, resolving the contradiction between providing complete information and maintaining network efficiency.
Solution Approach 2:
The system applies different quality standards to different comments by classifying them based on relevance to user interests and sentiment analysis. Comments are processed with varying levels of detail and priority according to their local quality characteristics, allowing the system to focus network resources on high-value comments while reducing transmission of low-value content.
2Measurement precision
If sentiment analysis and classification are performed on all comments, then comment relevance is improved, but processing time increases
Solution Approach 1:
The system segments the comment processing task into distinct stages: initial filtering based on user preferences, sentiment analysis of filtered comments, and final selection based on relevance scoring. This segmentation allows sentiment analysis to be applied only to a subset of comments rather than all comments, improving relevance accuracy while reducing overall processing time.
Solution Approach 2:
The system performs preliminary filtering of comments based on user preferences and basic relevance criteria before conducting sentiment analysis. This preliminary action reduces the volume of comments that require intensive sentiment analysis, thereby maintaining measurement precision for relevant comments while minimizing the time loss associated with processing all comments.
3Adaptability or versatility
If user profiles are generated using extensive user activity data, then personalization accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential features and patterns from extensive user activity data that are most relevant for personalization, rather than processing all raw data. This extraction approach maintains personalization accuracy by focusing on key user preferences and behaviors while reducing data processing complexity through selective feature extraction.
Data Source
AI summary
Aspects of the present invention disclose a method for managing aggregated comments of a forwarded media based on the sentiments of the forwarded media and relevance to a user. The method includes one or more processors identifying a user based at least in part on information provided by the user. The method further includes accessing user activity data utilizing the information provided by the user. The method further includes generating a first profile corresponding to the user based at least in part on the user activity data. The method further includes determining one or more preferences of the user based at least in part on the generated first profile. The method further includes identifying segments of textual data of forwarded media the user receives based at least in part on the preferences of the user, wherein the textual data corresponds to a comment associated with the forwarded media.