Comparison of Text Classification methods for Twitter Sentiment Analysis

Suresh Trupthi, Pabboju
2017 Narasimha International Journal of Computer & Mathematical Sciences IJCMS   unpublished
The problem of sentiment analysis for twitter data to classify tweets according to the sentiment expressed in them: positive, negative or neutral and 8 emotions as anger, surprise, disgust, anticipation, fear, joy, sadness, trust. it also does comparison between six different machine learning algorithms. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over
more » ... g service with over 200 million registered users-out of which 100 million are active users and half of them log on twitter on a daily basis-generating nearly 250million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The proposed work is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.