Remark Labelling is only available from V5.0.8.0 of EyeQuestion
Introduction
Open-ended comments provide valuable insights into the consumer's perception of a product, and help in identifying areas for improvement. However, analyzing such comments can be time-consuming and challenging. With remark labeling, the comments can be easily categorized and analyzed, which saves time and effort while providing meaningful insights.
This feature require panelists to provide detailed feedback about a product using a remark question type, which can be labeled and analyzed to extract meaningful insights.
With this functionality, users can assign specific labels to open-ended comments that panelists have provided about a product. These labels can be anything from positive or negative sentiments, to specific product characteristics or attributes. Once the comments have been labeled, the labels can be used as categories to analyze the frequency of the mentions of a particular product for that specific label using EyeOpenR.
Set Up Your Questionnaire
To use the Remark Labelling functionality the questionnaire must contain at least one question of the type General Question --> G-Text or H- Remark/Comment which allows you to collect open ended answer from your consumers or panelists.
When answers have been collected, go to the export tab and select Remark Labeling.
Create as many labels as you want, there is no limitation.
Select the question you want to add the labels to.
Select the label you want to allocate to which specific comment and click "Confirm".
Analysis through EyeOpenR
Once have you labeled all your remarks, the data can be analyzed in EyeOpenR.
Data can be threated as CATA data, and analysis such as the Cochran and McNemar analysis can be done.
In Counts (pairwise) : The table and graph display the number of times a label appears per product.
Related Articles
Napping Analysis
Purpose To provide an analysis of data collected using the napping methodology. Data Format Napping.xlsx For EyeOpenR to read your data the first five columns must include the following in the specified order: Assessor, Product, Session, Replica and ...
Same/Different Test Analysis
Available from version: 5.0.8.6 Purpose The Same Different Test is a discrimination test that is a variation of the paired comparison test. The assessor is presented with two samples and is asked to decide whether these samples are the same or ...
Correspondence Analysis (CATA and categorical data)
Purpose To visualise and summarise analyse tabular data and to highlight the patterns of association in two way tables. It is widely used for mapping pure qualitative variables – e.g cluster by demographic use. This is an example of typical data that ...
Correspondence Analysis (CATA and categorical data)
Purpose To visualise and summarise analyse tabular data and to highlight the patterns of association in two way tables. It is widely used for mapping pure qualitative variables – e.g cluster by demographic use. This is an example of typical data that ...
MAM Model Analysis
Purpose This analysis looks at the overall panel performance in terms of Discrimination, Agreement and Repeatability or Reproducibility, and then the performance of each individual in the panel in these terms. Using a more sophisticated model, than ...