Sentiment Analysis Time Zone Assignment via Fabricated Events
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Solution Overview
Problem
Existing methods fail to accurately untangle sentiment responses from users in different time zones due to rolling broadcasts, leading to muddled sentiment analysis, as location information from social media data is not always reliable or available, complicating the interpretation of event popularity across multiple time zones.
Innovation Solution
A method that analyzes social media inputs to determine sentiment values, forms user groups based on similar sentiments, and uses location information to assign time zones, supplementing with fabricated events to verify time zone placement and compute confidence values, employing techniques like recurrent neural networks for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If location information from social media data is used to assign time zones to user groups, then time zone assignment can be performed, but the reliability of sentiment analysis deteriorates because location information is not always reliable or available
Solution Approach 1:
The system performs preliminary actions by inserting fabricated events into the broadcast program before analyzing user responses. These fabricated events serve as verification mechanisms to pre-validate time zone assignments before they are used for sentiment analysis, thereby improving reliability without sacrificing operational ease
Solution Approach 2:
The system implements feedback by using user responses to fabricated events to verify and refine time zone assignments. The confidence value computed from comparing predicted sentiment values with actual user responses provides feedback that confirms or corrects time zone assignments, ensuring reliable sentiment analysis
2Reliability
If fabricated events are inserted to verify time zone placement, then confidence values can be computed to improve reliability, but the complexity of the system increases
Solution Approach 1:
The system uses fabricated events as intermediaries to verify time zone assignments. These events act as mediators between the broadcast system and user responses, enabling confidence computation without requiring direct complex interactions between the system components
Solution Approach 2:
The system creates simplified copies of real events through fabricated events that have known predicted sentiment values. These copies serve as test cases for verification without requiring the full complexity of analyzing organic user responses to unknown events
3Measurement precision
If user groups are formed based on similar sentiment values, then sentiment analysis accuracy improves, but the difficulty of detecting and measuring increases due to the need for tolerance value comparisons
Solution Approach 1:
The system changes parameters by introducing a tolerance value parameter that defines the threshold for grouping users with similar sentiments. This parameter transformation converts a complex multi-dimensional sentiment comparison problem into a simpler threshold-based classification that maintains accuracy while reducing measurement difficulty
Data Source
AI summary
Social media inputs from users, and specific to a content of a broadcast program, are analyzed to determine a sentiment value expressed relative to a portion of the content by a user. A group is formed of those users whose sentiment values expressed relative to the portion within a defined period during the broadcast of the program are within a specified tolerance value of sentiment value expressed by the user. A time zone of a location extracted from a social media input of the user is assigned to the group. A fabricated event that evokes a predicted sentiment is inserted in the broadcast program. A confidence value for the time zone is computed by comparing a sentiment value of a response by the group to the fabricated event with the predicted sentiment value. The group's placement in the time zone is confirmed when the confidence value exceeds a threshold.