ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 2, February 2014 Copyright to IJIRCCE www.ijircce.com 3182 Case Study on Online Reviews Sentiment Analysis Using Machine Learning Algorithms I.Hemalatha 1 , Dr. G.P.S.Varma 2 , Dr. A.Govardhan 3 Assistant Professor, Dept of IT, S.R.K.R. Engineering College, Bhimavaram, India 1 Professor, HOD, Dept of IT, S.R.K.R. Engineering College, Bhimavaram, India 1 Professor & Director of SIT, JNTU Hyderabad, India 3 ABSTRACT: The main objective of the research paper is to prove the effectiveness of Analyzing social media data. Twitter is a valuable resource for data mining because of its prevalence and recognition by famous persons. In this paper we present a system which collects Tweets from social networking sites, we’ll be able to do analysis using machine learning techniques on those Tweets and thus provide some prediction of business intelligence. Results of trend analysis will be display as tweets with different sections presenting positive, Negative and neutral. KEYWORDS: Pre-processing, Sentiment analysis, Classification. I. INTRODUCTION Sentiment analysis has been an important topic for data mining, while the prevailing of social networking, more and more tweet analysis research focuses on social networking. Many people use Twitter as the media for sharing information, driven the wave of using Twitter as a communication tools, which makes sentiment analysis on Twitter become a valuable topic for further discussion. In this paper we introduce a sentiment analysis tool, it comprises three functions: sentiment analysis among Twitter tweets, finding positive, negative and neutral tweets from information resources. This tool focuses on analyzing tweets from those media sites, thus provide a way to find out technology trends in the future. II. RELATED WORK A. Social Network Analysis Social network analysis is a methodology mainly developed by sociologists and researchers in social psychology. Social network analysis views social relationships in terms of network theory, while individual actor being seen as a node and relationship between each node are presented as an edge. Social network analysis has been define in [1] as an assumption of the importance of relationships among interacting units, and the relations defined by linkages among units are a fundamental component of network theories. Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics 1. In 1954, Barnes [2] started to use the term systematically to denote patterns of ties, encompassing concepts traditionally. Afterwards, there are many scholars expanded the use of systematic social network analysis. Due to the growth of online social networking site, online social networking analysis becomes a hot research topic recently. B. Twitter Twitter is an online social network used by millions of people around the world to be connected with their friends, family and colleagues through their computers and mobile phones [3]. The interface allows users to post short messages (up to 140 characters) that can be read by any other Twitter user. Users declare the people they are interested in following, in which case they get notified when that person has posted a new message. A user who is being followed by another user need not necessarily reciprocate by following them back, which renders the links of the network as directed. Twitter is categorized as a micro-blogging service. Micro-blogging is a form of blogging that allows users to send brief text updates or other media such as photographs or audio clips. Among variety of microblogging include Twitter, Plurk, Tumblr, Emote.in, Squeelr, Jaiku, identi.ca, and others, Twitter contains an enormous number of text