Question 1

How do I create a custom model on DeepOpinion Studio?

Accepted Answer

Create your own custom text analysis model easily on DeepOpinion Studio in a few hours. or even minutes if you already have training data! This tutorial will follow the steps shown in the diagram below.

https://malcolm-en-gb.s3.eu-west-1.amazonaws.com/instances/oiJS2eQZyE/resources/dRKsa3NnnM/Workflow_Graphic_AutoNLU-2525951.png (Workflow_Graphic_AutoNLU-2525951.png)

This short tutorial explains how to a custom model for analyzing hotels reviews. The same steps can be followed to customize any model. You can download this Hotel Reviews Training Data to do it yourself.

Follow this step-by-step guide to learn how:

Leave us a comment you would like more details.

Question 2

How do I create a label model for customer support ticket categorization on DeepOpinion Studio

Accepted Answer

Create your own customer support ticket categorization AI model easily on DeepOpinion Studio in minutes! This tutorial will follow the steps shown in the diagram below. As we will be using historical data from customer support we will skip the "ANNOTATE" step.

https://malcolm-en-gb.s3.eu-west-1.amazonaws.com/instances/oiJS2eQZyE/resources/dRKsa3NnnM/Workflow_Graphic_AutoNLU-2525951.png (Workflow_Graphic_AutoNLU-2525951.png)

This short tutorial explains how to train an AI model on categorizing customer support tickets from the banking industry. For example, we want to determine what the request is about (eg, lost credit card, banking app not working, etc) so that it gets directly assigned to the responsible team to handle it. The same steps can be followed to train any label model where one 1 label is only assigned to a document.

Important: download to follow the tutorial and do it yourself.

Click Here to Download The Banking Customer Support Tickets Training Data

Follow this step-by-step guide to learn how:

Leave us a comment you would like more details.

Question 3

How Do I Automate My Customer Service Requests on Freshdesk

Accepted Answer

Automate your Freshdesk tickets routing, tagging, and prioritization DeepOpinion Studio in 4 simple steps.

The 3 steps are:

Creat a free Freshdesk Support Account
Select your input trigger, which in this case is receiving a new ticket on freshdesk
Choose the model you want to perform the ticket categorization. You can also train a model on your freshdesk tickets historical data
Select your output, which in this case would be updating a field on the freshdesk ticket

Follow this step-by-step guide to learn how:

Leave us a comment you would like more details.

Question 4

How Do I Create A Freshdesk Ticket Automation on DeepOpinion Studio?

Accepted Answer

Automate your Freshdesk tickets routing, tagging, and prioritization DeepOpinion Studio in 3 simple steps.

The 3 steps are:

Select your input trigger, which in this case is receiving a new ticket on freshdesk
Choose the model you want to perform the ticket categorization. You can also train a model on your freshdesk tickets historical data
Select your output, which in this case would be updating a field on the freshdesk ticket

Follow this step-by-step guide to learn how:

Leave us a comment you would like more details.

Question 5

How can I annotate my data efficiently?

Accepted Answer

Summary

We made the building of an AI model super easy by creating Studio. You have probably already seen on Studio that this is a 4 simple steps process:

Upload Data ---> Define Aspects ---> Annotate Data ---> Start training

After automating the training process, we left only one task for the user: to label, or in other words to annotate, data.

Nevertheless, manual annotation is still a step that demands:

some background knowledge
some of your time for its completeness

In this article, you will learn everything you need to know for annotating data quickly, efficiently & correctly.

Text Annotation Guidelines

Annotators help AI models associate text segments to tags (topics & sentiments). To achieve that more efficiently, they should:

Involve domain experts in the process
Define topics to be comprehensive and non-overlapping in coverage
Write definitions for each tag and add examples
Annotate based on explicit mentions of topics with sentiments (= opinion)
Ignore implicit and subjective topics and factual statements
Focus on producing high-quality data, take breaks to refresh focus
Iterate between annotating and training to speed up the process To shed more light on the process, we mention a few examples

Below you can see how someone can define some aspects in a gsheet efficiently. Firstly, create larger groups to categorize the aspects, and then determine the aspects by providing an example and a description of each case.

https://malcolm-en-gb.s3.eu-west-1.amazonaws.com/instances/oiJS2eQZyE/resources/wPQ58HpA2Y/Screenshot%20from%202021-05-25%2010-12-37.png (Screenshot from 2021-05-25 10-12-37.png)

To shed more light on the process, we mention below two good practices along with exemplary cases.

Be careful only to annotate explicitly mentioned topics with sentiments. AI models can only process and learn from the information that is present in the text.
Try to ignore implicit and subjective or factual segments. Ignoring subjectivity and factual statements improves the quality of the opinion mining model.

Example 1: Facts have no sentiment, so they shouldn’t be annotated.

Example 2: No opinion was explicitly expressed about Delivery.

Example 3 - Not being able to open a bottle makes it not easy to use. It might have a nice design.

Example 4 - The customer received damaged quality. No mention of packaging.