AI-Driven UX Data Segmentation and Sentiment Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing user experience (UX) studies face challenges in efficiently collecting and analyzing large volumes of data, with existing systems being cumbersome and unreliable, particularly in determining the quality and relevance of data for UX researchers.
Innovation Solution
The implementation of AI-driven systems that store data in a standardized format, allowing real-time access and analysis, including segmentation, sentiment analysis, and semantic pairing of data, to generate insights and automatically curate relevant video and text clips, enabling filtering and clustering of data for improved UX research.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mass online surveys are used to collect user feedback information, then the quantity of information collected is increased, but the data becomes unwieldy and difficult to analyze
Solution Approach 1:
The system segments the collected user feedback data into standardized fields and categories, breaking down the unwieldy mass data into manageable, structured components that can be systematically analyzed and processed
2Reliability
If focus groups are used to assess website appeal and user friendliness, then qualitative insights are obtained, but the process is long, expensive and not reliable
Solution Approach 1:
The system enables participants to complete surveys and provide feedback independently without moderator intervention, allowing self-paced completion that expands the pool of respondents and reduces the time and cost associated with traditional focus group facilitation
Solution Approach 2:
The system replaces the mechanical facilitation and manual analysis processes of focus groups with automated digital survey administration and AI-driven data processing, eliminating human bottlenecks and scaling the process efficiently
3Measurement precision
If manual analysis of user experience data is performed, then data quality can be assessed, but the process becomes cumbersome and inefficient
Solution Approach 1:
The system replaces manual data analysis with automated processing that standardizes data formats, identifies outliers, and generates insights through algorithmic evaluation, dramatically improving productivity while maintaining measurement precision through consistent, repeatable criteria
Data Source
AI summary
Systems and methods for improving the analysis of results from a user experience study are provided. In some embodiments, study results are converted into text if needed. The text is segmented, and qualifiers and entity pairs are identified within the text (semantic analysis). Sentiment for these pairs is also determined. This information is utilized to generate one or more of different analyses of the study results. This includes, for example, the automatic generation of video and/or text clips from the study results that are of greatest interest to the user. This is performed by scoring certain keywords in the segments, generating the clips based upon the scores, and then filtering using trained machine learning models. Another analysis includes the generation of a semantic matrix with sentiment color coding. Intensity of the color may be defined by the frequency the entity-qualifier pair occurs.