Sentiment Analysis of Twitter Data using NLTK in Python

Abstract

In today’s world, Social Networking website like Twitter, Facebook, Tumbler, etc. plays a very significant role. Twitter is a micro-blogging platform which provides a tremendous amount of data which can be used for various applications of Sentiment Analysis like predictions, reviews, elections, marketing, etc. Sentiment Analysis is a process of extracting information from large amount of data, and classifies them into different classes called sentiments. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provide graphical demonstration for representing various results or trends and it also provide sample data to train and test various classifiers respectively. The goal of this thesis is to classify twitter data into sentiments (positive or negative) by using different supervised machine learning classifiers on data collected for different Indian political parties and to show which political party is performing best for public. We also concluded which classifier gives more accuracy during classification.