Calendar Visualization for Sentiment Data Analysis
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Solution Overview
Problem
Analyzing large volumes of customer feedback across multiple attributes is complex and time-consuming, especially when visualizing sentiment data over time, due to the sheer volume of data and varying attributes of interest.
Innovation Solution
A calendar graphical visualization is employed, using pixels to represent sentiments in data records, with time dimensions represented by columns and rows for different attributes, and gaps introduced to align time positions, allowing for easy correlation and visualization of sentiment trends across attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to analyze large volumes of customer feedback data, then comprehensive analysis can be performed, but the process becomes complex and time-consuming
Solution Approach 1:
The patent segments the large volume of customer feedback data into individual data records, each represented by a pixel in the calendar visualization. This segmentation allows analysts to process and visualize data in manageable units rather than overwhelming raw text, improving analysis efficiency while maintaining comprehensive coverage of all feedback
Solution Approach 2:
The patent transforms one-dimensional text data into a two-dimensional calendar visualization where each pixel represents a data record. The visualization adds temporal and spatial dimensions to the data, allowing analysts to quickly perceive patterns, trends, and correlations across multiple attributes without manually processing each record, thus resolving the contradiction between comprehensive analysis and analysis complexity
2Loss of information
If detailed sentiment analysis across multiple attributes is performed, then comprehensive insights are obtained, but visualization becomes overwhelming and difficult to interpret
Solution Approach 1:
The patent employs color-coded pixels in the calendar visualization to represent different sentiment categories (positive, negative, neutral) and multiple attributes simultaneously. This visual encoding allows comprehensive sentiment information across multiple attributes to be displayed in a single, easily interpretable visualization, resolving the contradiction between information completeness and interpretability
Solution Approach 2:
By transforming detailed sentiment data into a spatial calendar grid where position, color, and pattern encode different attributes, the patent enables comprehensive information to be presented in an intuitively interpretable format. Analysts can quickly grasp sentiment trends across multiple attributes by visually scanning the calendar rather than parsing detailed tables, thus maintaining information completeness while dramatically improving ease of interpretation
Data Source
AI summary
A calendar graphical visualization is generated that includes an arrangement of blocks including pixels representing data records containing user feedback, wherein plural groups of the blocks represent different attributes of the data records, and wherein the blocks correspond to respective time intervals. A size of the blocks is determined based on identifying a union of time positions corresponding to data records received for the different attributes in a particular time interval of the time intervals. Pixels in a first of the blocks corresponding to a first of the attributes are aligned with pixels in a second of the blocks corresponding to a second of the attributes by placing gaps in the first and second blocks at respective time positions that are missing values for corresponding ones of the attributes.