Sentiment Analysis System for Media Content Classification
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Solution Overview
Problem
Users face difficulty in assessing overall sentiment about media content due to the large volume of comments, making it time-consuming to determine user opinion or sentiment expressed within comments associated with media hosted on websites.
Innovation Solution
A computer-implemented method generates sentiment scores and profiles for media content based on comments, allowing users to search and browse media content by sentiment type, and display sentiment scores for sub-portions of media content, facilitating easier assessment and retrieval of sentiment-related media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users read all comments to assess overall sentiment, then they can obtain complete user opinion information, but it consumes excessive time and is difficult to manage due to large volume of comments
Solution Approach 1:
The patent introduces an intermediary system (sentiment analysis system) that processes comments and generates sentiment summaries. This intermediary automatically analyzes the text content of comments, extracts sentiment information, and presents it in a condensed format, thereby preserving complete sentiment information while eliminating the time-consuming manual reading process
Solution Approach 2:
The system extracts the essential sentiment information from the large volume of comments by identifying and isolating key sentiment-bearing elements. It separates the relevant sentiment data from the overwhelming bulk of comment text, presenting only the extracted sentiment patterns and trends to users, thus maintaining information completeness while reducing time investment
2Measurement precision
If the system provides detailed sentiment analysis of all comments, then users can assess overall sentiment accurately, but the system complexity increases due to processing large volumes of comments
Solution Approach 1:
The patent segments the comment processing task into distinct stages: initial filtering of comments, extraction of sentiment-bearing phrases, classification of sentiment types, and aggregation of results. This segmentation divides the complex processing of large comment volumes into manageable modules, maintaining measurement precision while reducing overall system complexity through structured decomposition
Solution Approach 2:
The system performs partial analysis by focusing only on the most sentiment-relevant portions of comments rather than analyzing every word equally. It applies sentiment detection selectively to key phrases and expressions, achieving accurate sentiment assessment without the computational overhead of exhaustive comment processing
3Ease of operation
If users manually review comments to find sentiment information, then they can identify user opinions, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service sentiment analysis by automatically performing the sentiment assessment function that would otherwise require manual user effort. The automated system serves itself by processing comments, generating sentiment summaries, and presenting results without requiring users to manually review individual comments, thereby improving both ease of operation and productivity
Data Source
AI summary
A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.