Let’s dig in our email!

As many of you, even if we are almost in 2018, I still work A LOT using emails and recently I was asking myself the following question what if I can leverage analytics and also machine learning to have a better understanding of my emails?

text-analytics

Here is a quick way to understand who is inspiring you more

positive-attitude

and who are instead the ones spreading a bit more negativity in your daily job 🙂

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You will need (if you want to process ALL your emails in one shot!) :

  1. Windows 7/8/10
  2. Outlook 2013 or 2016
  3. Access 2013 or 2016
  4. An Azure Subscription
  5. A data lake store and analytics account
  6. PowerBI Desktop or any other Visualization Tool you like (Tableau or simply Excel)

Step 1 : Link MS Access Tables to your Outlook folders as explained here

Step 2: Export from Access to csv files your emails.

Step 3: Upload those files to your data lake store.

Step 4: Process the fields containing text data with the U-SQL cognitive extensions and derive sentiment and key phrases of each email

Step 5: With PowerBI Desktop you can access the output data sitting into the data lake store as described here

Step 6: Find the senders with highest average sentiment and the ones with the lowest one 🙂 .

job-well-done-clipart-1

If you are worried about leaving your emails in the cloud, after obtaining the sentiment and key phrases , you can download this latest output and remove all the data from data lake store , using this (local) file as input for power bi desktop.

In addition to this I would also suggest to perform a one way hash of the sender email address and upload to the data lake store account the emails with this hashed field instead of the real sender.

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Once you have the data lake analytics job results you can download them and join locally in Access to associate again each email to the original sender.

 

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