Awesome, am really glad you found the articles useful.

Regarding your question, there are many approaches to doing emotion classification, each has some advantages and disadvantages, I strongly recommending reading a book on Data Mining to get a better holistic picture about the over-all discipline, and maybe that will also spark some unique ideas of your own, which will make for a great thesis!. There is a good book by Bing Liu on Web Data Mining I would recommend for this task.

As far as your specific problem is concerned, it looks like you are looking at classifying based on Plutchik’s wheel of emotions, in which he identifies 8 primal emotions, and uses those to build more complex human emotions. There are already some classification APIs that returns results based on this wheel of emotion, I recommend looking at ConveyAPI’s Emotion classifier as a practical example.

As far as building a classifier is concerned, assuming you are building a “supervised learning” classifier, you will need 2 things:

1) A training corpus of already emotion classified text.

There are a few ways you could build this corpus, for example:

• You could try cleaning up the data in the Experience Project.
• Using transcripts on TED, you can search by emotions such as Courageous, Inspiring, Funny, etc.
• Build your own corpus using Amazon’s Mechanical Turk. Cheap and easier to setup than one might think.
• Although not really recommended, but you could sign-up to a trial on one of the online APIs that offer emotion classification, and use your free trial to build a corpus. I am sure if you are using the data for research purpose it won’t cause too many issues, but the draw back is that you are building on something with an inherent inaccuracy, this inaccuracy will potentially be compounded when you building your own classifier on top of that data-set.
• Get creative!

2) A classification engine.

There are many packages out there that really simplifies the whole process, for example R (for advance users) or NLTK (for beginners/mid-level users). Each of those packages have great examples on how to get started, and a huge user community with many blog posts that can provide you with already made code. If you decide to go for NLTK I really recommend going through Jacob Perkins’ Streamhacker blog, he has so many examples on NLTK, and explains things very well.

Once you have those 2 elements, then begins the process of refining the algorithm and results to achieve the desired accuracy, my posts on improving your classification algorithm, or measuring accuracy using the confusion matrix, should aid you some of the way there, or at least give you some ideas on how to get started. There are many interesting articles online if you do some research.

It is important to note that all of this might help you get started, but in order to produce something totally unique and awesome, you will need to dig deep and understand the mathematical models and concepts these algorithms operate on, and read some of the very interesting research out there on various approaches and the results they produced.

I hope this all makes sense, please do not hesitate to hit me up if you need more information.

Goodluck with your thesis!

Cheers,