Saturday, September 23, 2017

What's a good topic for a bachelor's thesis in Sentiment Analysis?


Over the past few months (soon close to a year) you, my readers, might have noticed decline in frequency of my blogging. There are few reasons, including practical (absence of time), but still the most two important are:

1. Blogger has not developed too much as a tool over time. It probably continues to be relatively popular and bringing some ad money, so Google did not shut it down. Moving over to might be a better idea in order to produce visually "shinier" posts and actually enjoy writing.

2. There are other interesting and more interactive ways to share one's knowledge. One of such, that I personally like, is The site offers a reverse model compared to blogging: you answer questions. This way you ensure, that at least the questioner will read your answer, but so might do other respondents. Rating of your answers is another component, that contributes to statistics and getting analogy of payment - credits, that you can later use for instance for boosting your answers to a larger audience. But I would say the latter is of lesser importance to me.

Since I have never actually figured out, whether Quora allows you to read posts without being registered, re-posting my answers here from time to time could be a good way to also maintain this blog alive.

So here we go (slightly edited version):

What's a good topic for a bachelor's thesis in Sentiment Analysis?

Apart from applying deep neural networks to sentiment analysis being exciting, another topic that is exciting both from research and practice perspective is sarcasm detection. It goes somewhat outside of the topic of sentiment analysis per se out to the opinion mining. Sentiment analysis precision and recall are affected by the sarcastic posts. This is because sarcastic posts tend to be positive on the surface (in fact to the conventional algorithms — ML based or rule-based ones), but suggest negative context.
There are interesting situations that arise as a result of failing to recognize sarcasm. Borrowing from [1]:

User 1 tweet:

You are doing great! Who could predict heavy travel between #Thanksgiving and #NewYearsEve. And bad cold weather in Dec! Crazy!

Response from a major U.S. Airline:

We #love the kind words! Thanks so much.

User 1:

wow, just wow, I guess I should have #sarcasm

User 2:

Ahhh..**** reps. Just had a stellar experience w them at Westchester, NY last week. #CustomerSvcFail

Response from a major U.S. Airline:

Thanks for the shout-out Bonnie. We’re happy to hear you had a #stellar experience flying with us. Have a great day.

User 2:

You misinterpreted my dripping sarcasm. My experience at Westchester was 1 of the worst I’ve had with ****. And there are many.
A. et al. Sarcasm Detection on Twitter: A Behavioral Modeling Approach Sarcasm Detection on Twitter