Question 1

How Do I Connect PowerBI to DeepOpinion Studio Automation

Accepted Answer

Why Connect PowerBI to DeepOpinion Studio

As a DeepOpinion Studio user you are automating your text based processes. Depending on your work this can be intelligent routing of customer support tickets or customer review analysis or even insurance claim processing. In some cases, you might be interested in building reports and live dashboards out of this data. Many users prefer to customize their own dashboard or perhaps feed this data to an existing dashboard to have a single source of truth. PowerBI happens to be the BI tool of choice for many of our customers. Therefore, this integration makes it super easy to pull data to PowerBI to make you able to tell your data story.

How to Connect PowerBI to DeepOpinion Studio

There are two main steps to complete the PowerBI integration:

Create an automation on DeepOpinion Studio with PowerBI as the output step
Use PowerBI Query to get the data from DeepOpinion Studio

Step 1 Create an Automation

The first step is to create an automation. An automation consists of an input that has the raw data, a DeepOpinion AI model to analyze the data, and an output where the analysis results will be automatically sent. For example, if you would like to monitor your customer support, an input might be your customer support tickets from Zendesk, your model can be opinion mining, and your output can be sent to PowerBI. Alternatively, you can choose to trigger this automation manually which allows you to upload data on-demand and it will be directly reflected on PowerBI.

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Once you select PowerBI as an output a screen will slide in explaining the instructions. Here you just have to copy the query that you will later add to your PowerBI account to get the results of this specific model.

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Step 2 Create a PowerBI Customer Query

Now move to PowerBI, click on "Get Data" on the top menu and choose "Blank Query" from the drop down list.

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An empty window will pop up, click on "Advanced Editor" at the centre of the top menu and another small window will pop up. Here on the advanced editor is where you will paste the query you copied from DeepOpinion Studio and then click on "Done" on the bottom of this tiny window.

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Now by this, all the analysis that has been performed by this specific model will be pulled from DeepOpinion Studio. Then click on "Close & Apply" on the top right. All future analysis will be updated here as well when you open PowerBI again.

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Now you are ready to do your magic with data visualization on PowerBI. We look forward to hearing about it!

Question 2

What is The Data Structure of Batch Analysis Results for Opinion Mining

Accepted Answer

What is Aspect-based Sentiment Analysis

Aspect-based sentiment analysis refers to the task of identifying fine-grained opinion towards a specific topic in text. This text can take a variety of formats such as emails, product reviews, customer requests, social media posts, and any other user generated content. This approach to text analysis is considered the industry standard for analyzing customer reviews as seen in the image below.

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How A Customer Review is Structured

A customer review consists of two main parts; the review text and review description or metadata such as name, date, rating, etc. This universally applies to most online reviews including Amazon product reviews, Google Maps company reviews, tweets, and more.

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Reviews tend to be dense with mentions of topics, emotions, and sentiments as customers are expressing their experience in a short message. You would hence find reviews that include multiple sentences. Therefore, we unpack a review to its core elements which we refer to as segments (eg, sentences). This way we ensure high accuracy and provide you with all the essential analysis you require to know about a review.

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To get fine-grained analysis the review is broken-down to 3 levels. Level 1 is the whole review message, Level 2 is a breakdown of the segments in the review text, and Level 3 are the topics and sentiments associated with each segment which can be none, one, or more. The diagram below illustrates this hierarchy.

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Your analysis results will also include a unique ID for each level to give it a convenient structure and make working with it easy in your desired application.

https://malcolm-en-gb.s3.eu-west-1.amazonaws.com/instances/oiJS2eQZyE/resources/O1A0Q5wNuX/Review_absa_structure.png (Review_absa_structure.png)

How Our Analysis Results are Structured

It is common to upload a file with 100 rows (1 review per row) and download results with 300 rows for example. This is because each of these reviews would have multiple sentences that can mention 1 or more topics. To make results easy to work with, we add a new row for each topic identified in each segment.

For example, if you have a review consisting of 2 sentences and each sentence has 2 topics mentioned, you will end with 4 rows for this review. The review text will appear 4 times, each segment will appear twice, and each topic is considered unique to a segment. This way you get a layered/nested results ready for your following activities.

Example of input file with 50 rows (50 reviews)

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Example of output file with 280 rows (50 reviews)

Having more rows in the results is an indicator of the richness of the data. You will also notice some column headers are highlighted in blue color. These columns are generated by DeepOpinion Studio.

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Results Data Dictionary

Here is a description of the columns you can expect from your analysis.

id [Level 1]: A system generated unique ID for each original row (document or review)

Text [Level 1]: The original text uploaded for automated analysis (document or review)

Segment id [Level 2]: A system generated unique ID for each segment in the original row (child of id)

Segment text [Level 2]: A segment of the original text uploaded for automated analysis (child of text)

Span start [Level 2]: The character count of where a segment starts (child of text)

Span end [Level 2]: The character count of where a segment ends (child of text)

Tag id [Level 3]: A system generated unique ID for each topic identified in a text segment (child of segment id)

Aspect [Level 3]: A topic identified in a text segment (child of segment text)

Sentiment [Level 3]: A sentiment category identified in a text segment (child of segment text)

Others [Level 1]: Any other column included in the original data such as dates, names, ratings, and source (metadata)

Why This Structure

This data structure has been carefully designed to make the results convenient to work with in downstream tasks such as in process automation or data visualization. For example, you might be interested in counting how often do your customers complain about battery life and auto-create a support ticket for each time this is mentioned.

https://malcolm-en-gb.s3.eu-west-1.amazonaws.com/instances/oiJS2eQZyE/resources/IGmg4MO3NV/Ticket_automation_freshdesk_animated.gif (Ticket_automation_freshdesk_animated.gif)

Contact us to know more about moving your customer review from static insights to dynamic and automated actions

How Do I Connect PowerBI to DeepOpinion Studio Automation

What is The Data Structure of Batch Analysis Results for Opinion Mining

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