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Emails, Web pages, social media, chat, survey responses, etc. are some of the channels through which customers communicate with a company or organization. The data or text messages coming through these channels are unstructured. Due to its unstructured nature it is difficult to extract insight like, whether the customer is happy or not, whether it is spam or not and what is the category of text. If we receive 100 mails from customers in a day and are asked to identify the percentage of positive and negative response then it will be very difficult and time consuming.
To solve this problem, we can now think of using Text classification model of AI Builder. In this blog, we will see how to use Text classification model to identify positive and negative responses coming through emails.
If you have not read our previous blog on AI Builder please go through it. You will learn how to enable AI Builder feature and how to create “Form processing model” to extract forms or documents.
Now let’s start with Text classification model.
The success of Text classification model depends on data that we are using to train the model. Before we build the Text classification model, we must check the prerequisites, data format of data that we are going to use to train the model. Check the following article for the same. https://docs.microsoft.com/en-us/ai-builder/before-you-build-text-classification-model
Basically, here we need to,
Below is the sample data that we have created to try this (Text classification model, AI Builder) feature,
Name column for primary key field, Tags column to store tags in semicolon separated and Text column to store text.
If you do not have sufficient data then model may not work correctly. So make sure that you have at least 50 text of each tags. For example, 50 different text of Positive tag and 50 different text of Negative tags.
Before publishing the model you can do the quick test.
We have imported 122 records through CSV file in a new entity called ‘Text Classification Data’ entity and when we publish the model a new entity named as ‘CustomerResponseClassification_Results’ gets created. So, for each Text classification data record, a corresponding CustomerResponseClassification_Results entity record is created with Confidence level as shown in the below screen shot:
Use Microsoft Flow to categorise the email content using Text classification model: Now we can use the running text classification model in Microsoft Flow where we can pass the text coming from email or any other channel through it and can read the response.
In Model, we have selected the model that we created for Text classification and added Request payload as shown in the below format;
That’s it, this flow will trigger on creation of email and then Predict action will run Text classification model on the email content and return the response in JSON format as shown in the below screen shot.
Good morning. We presented this product to our customer and they liked it. Good job.
We really need some urgent help. Please can someone look into this issue as early as possible? Users are getting lot of errors.
Now we can pass this JSON and read the suggested tags and its score. Read this blog to know how to read the response from Predict (Common Data Service) action.
AI Builder allows us to automate business process by adding artificial intelligence to it without having skills in Artificial intelligence or in data science.
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