All of our widgets follow the same pattern. You use the options tab to design your widget, for example pick the variables you want to chart, and then the filters tab to filter for a specific segmentation, if needed.
The table widget follows the same principle but it is a touch more complicated, so a more in-depth explanation is in order.
Basically, the table widget can be a simple table or a pivot table, but the only thing you can pivot by is date.
When setting up a simple table the first thing you can do is name your table in the “widget name” field. Here we have a dataset containing hotel reviews from various countries. I am going to create a table that primarily looks at the average price per country.
Next, you choose which variable to group by. This is the variable the data will be displayed by, basically the variable that will be in the rows of the table. In the dropdown you will find the different metadata variables contained in the original dataset along with the metadata supplied by Keatext: topics, opinion and tags.
Underneath you see the date field, you don't do anything with the date field for a simple table. If you look at the top right of the popup you will see that it is set to “all time”. A simple table is always set to “all time” as it is not breaking the data down into different time periods.
You can choose to show items with no value if you want to, or restrict the table to items that contain values, using the toggle.
Lastly, you must choose your values, what you want to calculate by. You can calculate by any metadata field in the original dataset, plus Keatext metrics such as comment count, record count or sentiment score etc.
The calculated field options will vary according to the variable chosen.
I have chosen to look at the average room price per country and for fun I threw in the average sentiment score per country.
From the table I can see that the best value - with a very high sentiment score of 9.72 and the cheapest average room price of $73 - is Canada. Now that's a good argument for going on vacation - in the summer ;) !
From here you can filter your chart by moving to the filters tab if you want to segment the data to exclude certain information. For example if I only wanted to look at European countries I could filter out the United States, Australia and Canada.
Now let’s pivot that table!
Like I said previously, you can only pivot by time period. I removed the value “sentiment score” (just to make room on the table, you can keep it if you want to), and changed the date granularity to “year”. Now I can see the room price trends year over year.
So there you have it. The table widget is powerful. It can display a lot of information on both the simple table and the pivot table versions and can be tailored to illustrate the point your are emphasizing.