Featured Article on Nigel Frank: Azure, Bayesian Theorem and Text Analytics

Are you interested in Azure, Bayesian Theorem, text analytics or sentiment analysis?

I wrote a guest piece on the Nigel Frank International blog a few weeks ago that covers Bayesian Theorem and shows how it can be used to perform sentiment analysis.

I remember getting my head around this back in 2013 as part of a research project I was working on to help classify Twitter data.

It was quite a lot of work! I had to:

• get my head around Bayesian Theorem
• create a data model
• source training data
• cleanse and pre-process training data
• label training data
• build an API in C# to process the data
• iterating through various tests to improve the accuracy (the highest classification accuracy I hit was between 70-80%)

I ended up putting a web application in front of the C# API I built and submitted it to Twitter for an initiative I heard about:

(read about my submissions for 2016 and 2017)

Shortly after I had completed this, I started to notice Azure Cognitive Services Text Analytics and how a lot of this was made available out of the box!

In the end, I started to swap out my custom API and use the Azure Text Analytics API.  It saved me the headache of managing a data model, dealing with bug fixes, and sourcing training data and so on.

Anyway, that’s enough about the background!

In the guest blog I also:

• introduce a free Bayesian Classifier called “nBayes”
• talk about training data
• run through some examples
• discuss use cases

You can read the guest blog here.