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?
Here is a quick way to understand who is inspiring you more
and who are instead the ones spreading a bit more negativity in your daily job 🙂
You will need (if you want to process ALL your emails in one shot!) :
- Windows 7/8/10
- Outlook 2013 or 2016
- Access 2013 or 2016
- An Azure Subscription
- A data lake store and analytics account
- 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 🙂 .
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.
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.