After uploading your dataset to the Relevance AI's platform, you can check your data, data statistics and configure some settings.
In this page, we will explain the Setting dataset feature. The setting page allows you to configure field types, aliases, and more technical settings. To start click on your desired dataset under your account
From the menu on the left-hand side, click on "Settings" under Datasets and you will see a page containing items shown in the following two images.
There are 5 main sections to this page:
In this section, you can
- Hide one/some fields
If you wish to prevent a field from showing up on dashboards, simply tick Hide for that field
- Assign an alias to a field
Aliases can be used to refer to fields with another name. This feature is very useful in situations like when existing names are too long, not descriptive enough or a simple change of format as shown in the image (i.e. full_name -> Full Name)
- Enter a tooltip for a field
Sometimes not all users are aware of what data each field represent. You can add a tooltip for each field which will show up any time users hover over the field name on a dashboard
- Change the data type if necessary
Data types are detected automatically in our platform and it is best not to change them. However, in the very rare case of wrong type detection, use the marked down-arrow and select the correct data type from the list.
The use of the right words makes the insight and the final presentation more tangible and meaningful. If your dataset is on customer reviews, comments, data descriptions or project learnings, you can simply use the right referral to the entities here, (e.i. Reviews, Comments, Descriptions and Learnings respectively). Using the plural word form is recommended.
You are provided with date filtering and time series for insight analysis and presentation. Every data entry automatically receives an
insert_date_ field, the date the entity has been uploaded to Relevance AI's platform. In case, you wish to analyse with another date field in your dataset, select it from the drop-down menu as shown below.
Note: Keep in mind that date entries must follow dash-separated year, month and day format in 4, 2, and 2 digits (e.g. yyyy-mm-dd). Therefore, in case your dataset contains a date field with values not in the standard format, you need to standardize the date values before uploading your dataset to the platform. For instance,
May 29, 20 --> 2020-05-29
2017-08-21 15:56:48 ---> 2017-08-21
A very useful tool when dealing with text data is a highlighter. Relevance AI's highlighting tool receives a "Text field to highlight" and a "Substring" field where the substring is a subset of the main text. The text subset is highlighted when presenting/visualizing the parent field.
Text field to highlight and the substring field are selected via the marked drop-down menus in the picture below. Leave the weight as none or use the numeric values in your dataset as a weighing parameter if applicable (e.g. when analysing negativity or positivity of comments and darker highlights identify stronger views).
It is also possible to color-code substrings according to sentiment analysis results. More explanation is provided in the next section.
After running sentiment analysis workflow, a few fields will be added to your dataset. Select the sentiment substring and the sentiment values as substring field and weight under Text highlighting rules. Modify the range rules under Sentiment range rules.
Max negative value: any scores smaller than the set value will result in red highlighting
Min positive value: any scores larger than the set value will result in green highlighting
Updated 2 months ago