  As microblogging services like Twitter are becoming more and more influential
in today's globalised world, its facets like sentiment analysis are being
extensively studied. We are no longer constrained by our own opinion. Others
opinions and sentiments play a huge role in shaping our perspective. In this
paper, we build on previous works on Twitter sentiment analysis using Distant
Supervision. The existing approach requires huge computation resource for
analysing large number of tweets. In this paper, we propose techniques to speed
up the computation process for sentiment analysis. We use tweet subjectivity to
select the right training samples. We also introduce the concept of EFWS
(Effective Word Score) of a tweet that is derived from polarity scores of
frequently used words, which is an additional heuristic that can be used to
speed up the sentiment classification with standard machine learning
algorithms. We performed our experiments using 1.6 million tweets. Experimental
evaluations show that our proposed technique is more efficient and has higher
accuracy compared to previously proposed methods. We achieve overall accuracies
of around 80% (EFWS heuristic gives an accuracy around 85%) on a training
dataset of 100K tweets, which is half the size of the dataset used for the
baseline model. The accuracy of our proposed model is 2-3% higher than the
baseline model, and the model effectively trains at twice the speed of the
baseline model.
