The workshop would aim to provide a general overview of the concepts that are used in conducting a Sentiment Analysis on textual data.

The beginning 5 minutes of the talk would deal with how nltk is used in python, what corpus it provides, the stemmers inbuilt, sentence tokenisation and pickled models. I would then move to using this nltk toolkit for sentence tokenisation and pos tagging and how NER (Named-Entity Recognition can be useful for Aspect based sentiment analysis) which would take around 10 minutes.

I would then proceed to discuss about the classification methods like bag-of-words, random forests etc. and where and when they should be used. In here, I would also explain the bias induced in dataset regarding the industry it is dealing with. I would also touch briefly on binary classification (positive, negative) or probability value vector in case of multi-label classification. This would take 10 minutes.

I would then discuss about the various directions in which sentiment analysis is used, namely, stance detection, aspect based sentiment analysis etc. I would go over the various ares that sentiment analysis can be used (product reviews, social media posts) and how that information about sentiment can be used. And then I would conclude by discussing about the projects that I have worked upon, that is, giving AI the benefit of recognising and empathising with emotions and how it would be helpful.