International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4746 SENTIMENT ANALYSIS USING TWITTER DATA Kirti Jain 1 , Abhishek Singh 2 , Arushi Yadav 3 1 Asst. Professor, Dept. Of Computer Science, Inderprastha Engineering College 2, 3 Student, Dept. Of Computer Science, Inderprastha Engineering College Dr. A. P. J. Abdul Kalam Technical University ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Sentiment analysis as the name suggest it is the analysis of the sentiments/feelings/expression related to any topic, it is also known as opinion mining. Here the motive is to find the general sentiment associated to given document. We try to classify the subjective information gathered from some microblogging site according to its polarity such as positive, neutral and negative using machine learning and natural language processing. In this project, we chose Twitter as the microblogging source for getting peoples sentiments and try to classify the tweets into positive, neutral and negative sentiment. Key Words: Sentiment Analysis, Polarity, Machine Learning, Natural Language Processing, Twitter, Microblogging. 1.INTRODUCTION Sentiment analysis studies people judgment or thought towards certain entity. Twitter is a resourceful place to find people sentiment. Here peoples post their thought/experience/ feelings through tweets which has a character limit of 140. Twitter has a provision for developers to collect data from twitter by releasing their APIs. In this project we are using one of the twitter API i.e. streaming API which helps to extract the content in the real time. Here we perform the linguistic analysis by building the classifier using the several machine learning techniques and natural language processing by using the collected corpus from the Kaggle as the training data to train our classifier and use the streamed corpus as the testing data to test the result of our classifier to classify the different sentiments related to tweets. In this project we focus on the tweets related to airline as the customer shares their experience on the twitter thorough their tweets and our analyzer helps the airline company to improve their services by keeping an eye on people’s sentiments by overcoming their flaws. 2. LITERATURE REVIEW Table -1: Literature Survey Table S. NO Paper Title Authors y e a r Methods Remarks 1. Sentiment Analysis on Twitter Data Varsha Sahayak, Vijaya Shete, Apashabi Pathan 2 0 1 5 Naïve Bayes, Maximum Entropy, SVM In the survey, we found that social media related features can be used to predict sentiment in Twitter.