Abstract:
Twitter is amongst globally used micro-blogging applications by millions of users
on daily basis for sharing their thoughts regarding diverse topics of different
occasions as well as their opinions on the hottest trends. Which makes it a rich
source for decision making and sentiment analysis. Over the recent years, multiple
frameworks and models have been proposed to extract people’s sentiments against
a topic, an individual or an organization with to help decision making process and
still a lot of work is on the way to get accurate models. Sentiment analysis focuses
on the polarity calculation from a tweet/text and classifying them as positive,
neutral and negative. The primary focus of this methodology is to address the
neutral tweets along with positive and negative tweets and their automated polarity
generation. Here we proposed a system which extracts the tweets from Twitter
against a profile and after applying preprocessing techniques, calculating
sentiments and applying Random Forest, Naïve Bayes, Support Vector Machine,
Multinomial Logistic Regression Classifier and XGBoost for prediction of
sentiments from the tweets. The highest accuracy achieved with this methodology
is 81% accuracy.
