Unsupervised Sentiment Analysis

Question

I've been reading a lot of articles that explain the need for an initial set of texts that are classified as either 'positive' or 'negative' before a sentiment analysis system will really work.

My question is: Has anyone attempted just doing a rudimentary check of 'positive' adjectives vs 'negative' adjectives, taking into account any simple negators to avoid classing 'not happy' as positive? If so, are there any articles that discuss just why this strategy isn't realistic?

Answer 1

You can perform unsupervised sentiment analysis using python’s AFINN and NLTK libraries. You should check out this GitHub repository for detailed python code. This code gave an accuracy of 71%, which is pretty good in the case of unsupervised sentiment analysis.

Hope this answer helps.