Adjusting Sentiment Scores Using Author Baseline Attitude
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sentiment measurement systems for online content fail to accurately convey the attitude of an author, as they analyze content in isolation without considering the author's personality, mood, or demographic context, leading to reduced accuracy and usefulness.
Innovation Solution
A system that determines an author's baseline attitude by analyzing their prior online content, incorporating demographic profiles, and adjusting sentiment scores based on the author's personality, mood, and subject-matter attitudes to generate a more accurate adjusted sentiment score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw sentiment scoring is used to analyze online content in isolation, then the analysis process is simple and fast, but the accuracy of sentiment measurement is reduced
Solution Approach 1:
The system performs preliminary action by establishing a baseline attitude profile for each author before analyzing their new content. This baseline is built from historical content analysis and demographic information, allowing the system to pre-calculate contextual factors that will be applied when scoring new sentiment, thereby improving accuracy without adding complexity to the real-time analysis process
Solution Approach 2:
The patent introduces an intermediary baseline attitude profile that mediates between the raw sentiment score and the final adjusted sentiment score. This intermediary layer incorporates author personality, demographic context, and historical behavior to adjust the raw sentiment, resolving the contradiction by adding measurement precision through a structured intermediate step rather than direct complex analysis
2Measurement precision
If author baseline attitude is incorporated to adjust sentiment scores, then sentiment measurement accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The baseline attitude profile is constructed in advance from author demographics and historical content, storing pre-computed contextual factors in a database. When new content arrives, the system simply retrieves the pre-built baseline and applies it to adjust the raw sentiment score, avoiding time-consuming re-analysis of the author's entire history and minimizing processing time while maintaining improved accuracy
3Loss of information
If demographic profiles and historical content are analyzed to determine baseline attitude, then author context understanding is enhanced, but data processing complexity increases
Solution Approach 1:
The system extracts and separates the baseline attitude components (demographic factors, personality traits, historical sentiment patterns) from the actual content analysis process. By extracting these contextual factors into a distinct baseline profile that is stored and reused, the system retains comprehensive context information while simplifying the processing of new content, as the baseline is computed once and applied repeatedly without re-processing the underlying demographic and historical data
Data Source
AI summary
In an online environment, a baseline attitude of an author of online content is determined. Based on the baseline attitude and a raw sentiment score for an instance of online content, an adjusted sentiment score for the online content instance is generated. A variance from the baseline attitude may be detected, based on the online content of the author. In response to the variance, a current mood of the author is determined and, using the current mood and the raw sentiment score, another adjusted sentiment score for the online content instance is generated. The baseline attitude of the author may be determined using one or more of an analysis of the online content instance, a demographic profile of the author, and a subject matter area of the online content instance. The detection of the variance from the baseline attitude may incorporate a frequency of instances of online content.