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This is the answers users have typed in the index they have filled in, we call it free text but basically it is comments made by users on the questions in the indexes. This is part of the more complex functions within Temporall CW. We make use of Google’s Cloud Integration to build a sentiment analysis machine we call Temporall’s Sentiment Analysis.
Temporall's Sentiment Analysis inspects the comment and identifies the prevailing emotional opinion within the text, especially to determine a writer's attitude as positive, negative, or neutral. The analysis of the comment is reflected on a scale. 0 = N/A, 1,2 is Negative, 3 is Neutral and 4,5 is Positive. Temporall’s Sentiment Analysis sentiment scoring ranges are divided into the 4 following brackets.
Section 1
Free Text
In the main free text component, we have a custom-built filter by which you can filter the Free Text answers first by Macro Attribute (these include: eNPS, Culture, and Signals). To filter even further down, you can click on a Micro Attribute to view all of its questions.
See the following screenshots for a detailed explanation.
Choose a Macro Attribute.
Choose a Micro Attribute.
“Macro” - “Micro”. Listed as you have selected.
Question belonging to that Micro.
- This is an overview of scores each answer to this question received.
- Unscored - Not yet scored
- N/A - Should not be scored (based on question)
- Negative/Neutral/ Positive
First comment’s Analysis. Here you can see the user had a negative emotional opinion and Temporall’s Sentiment Analysis has a certainty of 2 out of 5 for that score. In this case that is not very high. But as the system progresses Temporall’s Sentiment Analysis will be trained to perform more accurate analyses as this is an Artificially Intelligent machine.
Section 2
Word Cloud
Here we provide some graphical material for the user, based on the top words that have been retrieved from the campaign answers. These are words that have been counted and are plotted here according to the number of iterations from highest to lowest. Highest being the biggest word. In this case, the most used word is people, followed by culture and business. Hovering over each word will display a tooltip with the number of occurrences the word was used in the index. This view can be exported to PNG or PDF using the more button in the top right corner.
Section 3
Mentions
Temporall CW’s Mentions are built-in to reference a person in mind. This means, if a person’s name is mentioned you can find all mentions to that name and investigate the comments made. This person could be tagged as an asset to the organization or maybe even a liability. In this way, you can identify persons and take actions according to the comments given or the feedback received.
The Mentions component also has an intuitive build, it allows for human error in regards to how a person’s name is spelt. If you by mistake misspelt a person’s name but it has a few similarities to the original correct spelling the algorithm will match it with the correct name. This allows you to see all mentions of people in the campaign. And as you can see in the screenshot below you can see the “incorrect” versions of the same name and then you have the option to see all the relevant mentions to that name.
This chart is a graphical representation of the number of mentions each name has received. Something to take note of, even if a person’s name is mentioned only one time it will still be shown here.
This is a referencing button, this will take you to the Culture Champions page.
As you can see here Poole has a unique surname and people tend to misspell this surname. The mentions algorithm ensures that all variations of this surname are still linked to the correct name for example Pool would also be linked to Loni.
This button allows you to view all the relevant comments to this person’s name. Here you can investigate all the comments and make your conclusion in regards to the actions you think necessary - if any.
Section 4
Mentions Network
The Mentions Network is a chart that has been built based on the number of times a person’s name has been mentioned in a campaign. This is not restricted by any means, this can be based on a complaint or a compliment.
Here you can see a graphical representation of all the mentions made in the campaign. When setting up a campaign you have the option of setting a default demographic. In this case, the default demographic is Region. See the next screenshot to learn how to filter according to a different demographic.
Clicking the more button in the top right corner brings up this options panel.
Here you can choose to filter the chart by the minimum number of mentions per person. So choosing 10 will display fewer people than choosing 1 because a person can be mentioned once easily.
All questions regarding mentions will display here, so you can choose which question you would like to filter your content.
As mentioned above, you can filter the chart according to different demographics. Click on the demographic you want to see.
You can toggle the labels to disappear or re-appear.
Here you have the option to download the chart as a PDF or PNG.
Section 5
Culture Champions
The Culture Champions is a chart that has been built based on the number of times a person’s name has been mentioned in a positive attitude. The reason for this is, to get the people within the organization that sets a good example of how the culture within the company should be. These people are often people that are friendly, helpful and a typical go-to guy/girl.
This chart shows all the mentions based on frequency, this is the most frequently used chart and therefore is placed first.
Total Mentions by gender - this is another demographic by which the same data can be displayed. As you go down the list of charts you can see the same data in different demographics. This allows for a very comprehensive summary of the people classified as Culture Champions.