IJSTE - International Journal of Science Technology & Engineering | Volume 1 | Issue 10 | April 2015 ISSN (online): 2349-784X All rights reserved by www.ijste.org 130 Sentiment Analysis of Tweets Pooja Kumari Shikha Singh Department of Computer Engineering Department of Computer Engineering Padmabhushan Vasantdada Patil Pratishthan’s College of Padmabhushan Vasantdada Patil Pratishthan’s College of Engineering, Eastern Express Highway, Near Everard Nagar, Engineering, Eastern Express Highway, Near Everard Nagar, Sion-Chunabhatti, Mumbai-400 022 Sion-Chunabhatti, Mumbai-400 022 Devika More Dakshata Talpade Department of Computer Engineering Department of Computer Engineering Padmabhushan Vasantdada Patil Pratishthan’s College of Padmabhushan Vasantdada Patil Pratishthan’s College of Engineering, Eastern Express Highway, Near Everard Nagar, Engineering, Eastern Express Highway, Near Everard Nagar, Sion-Chunabhatti, Mumbai-400 022 Sion-Chunabhatti, Mumbai-400 022 Manjiri Pathak Department of Computer Engineering Padmabhushan Vasantdada Patil Pratishthan’s College of Engineering, Eastern Express Highway, Near Everard Nagar, Sion-Chunabhatti, Mumbai-400 022 Abstract Microblogging websites such as twitter have evolved into source of unfettered and wide ranging category of information. Use of socially generated big data to access information about collective states of the minds in human societies becomes a new paradigm in the emerging field of computational social science. One of the natural applications of this would be prediction of the society's reaction to a new product in the sense of popularity and adoption rate. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. Using the corpus, we build a sentiment classifier that is able to determine positive, negative and neutral sentiments for a document. Keywords: Microblogging websites, Naive Bayes classifier ________________________________________________________________________________________________________ I. INTRODUCTION In recent past we noticed an outburst of data availability, the so-called data deluge, determined by an increased amount of social communication performed through different electronic channels. The emergence of internet and web 2.0 led to the outburst of social media providing people an opportunity to publicly share their thoughts and express their opinions. Social media technologies exist in different forms such as blogs, business networks, enterprise social networks, forums, microblogs, photo sharing, products/services review, social bookmarking, social gaming, social networks, video sharing and virtual worlds. Amongst these, microblogging websites have become a very well-known paradigm for communication. This is due to nature of microblogs on which people post real time messages about their opinions on a variety of topics, discuss current issues, complain, and express positive sentiment for products they use in daily life. Therefore microblogging web-sites have become rich sources of data for opinion mining and sentiment analysis. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state (the emotional state of the author while writing), or the intended emotional communication (the emotional effect the author wishes to have on the reader. Along with expressing views and opinions and providing us with novel answers to classic questions, the consummate analysis of this huge amount of data could have practical applications to predict, monitor, and cope with many different type of events, from simple matters of daily life to massive crises in the global scale. At analytical level there are several technological innovations that help making sense of the large amount of data availability. With around 300 million users sending out around 500 millions of micro-blogs (approximately) every day, Twitter is certainly an effective channel for communication. Additionally this social networking site is not just for teenagers or celebrities tweeting about their daily activities but has also emerged as a powerful marketing tool by many business owners who are using it to help their businesses grow. In this paper we will take into account of Twitter, the most popular microblogging platform for the task of sentiment analysis. and build models for classifying “tweets” into positive, negative and neutral sentiment. We build models for two classification tasks: a binary task of classifying sentiment into positive and negative classes and a 3-way task of classifying sentiment into positive, negative and neutral classes. The remainder of this paper is organized as follows: Section 2 briefly reviews the literature on forecasting the box-office success of theatrical movies. Section 3 gives the details of our methodology by specifically talking about the data, its collection, preprocessing and classification using Naive based classification method. Next, the results evaluated using our method and