IJSRST1845302 | Received : 20 March 2018 | Accepted : 05 April 2018 | March-April-2018 [ (4) 5 : 1133-1137] © 2018 IJSRST | Volume 4 | Issue 5 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 1133 Content Analysis in Social Network Analysis using Sentiment Analysis M. Thangaraj 1 , S. Amutha 2 * 1 Department of Computer Science, Madurai Kamaraj University, Madurai, Tamilnadu, India 2 Department of Computer Science, Manonmanaiam Sundaranar University, Tirunelveli, Tamilnadu, India ABSTRACT This paper shows about content from social network tools sites or social media tools like twitter or face book user document are analyzed with the help of Social network Analysis tools like Gephi, NodeXL and Pajek using positive, negative, neutral and emoticons. In Sentiment Analysis the text are classified in to various levels like word, sentence, phrase, and feature and document level. The content mining is divided into five categories. The major analysis is Sentiment Annotation, Sentiment Classification, Sentiment Detection, Sentiment Determination, Sentiment Extraction and Sentiment Lexicon. Keywords: Social network Analysis, Sentiment Analysis, Sentiment Annotation, Content Analysis, Sentiment Classification I. INTRODUCTION Social Network (SN) is a term used to express web- based services that let individuals to make a public / semi-public profile inside a domain such that they can communicatively bond with others users within the network through the text or images. It is enabling the structure and exchange of user –generated content. SN is a graph consisting of nodes and links used to represent Social relationship on Social Sites. SNs are important foundations of online interactions and contents sharing, subjectivity, assessments, approaches, evaluation, influences, observations, feelings, opinions and sentiments expression allow out in text, reviews, blogs, discussions, news, remarks, reactions or some other documents[1]. SN data is varying in size, noise and dynamism. SN sites are commonly known for information dissemination personal activates posting, product reviews, online pictures sharing, professional profiling, advertisements and opinion/ sentiment expression. Mostly current news alerts updates, breaking news political debates and government policy are also posted and analysed on SN sites. It allows users to express their views be it positive, negative, neutral or emoticons. SN based on virtual communities has begun to publish members’ public profile information, including social links, using the semantic web language like resource description framework (RDF). II. SEMANTIC WEB Web 2.0 has changed the technological landscape of the Internet computing world today. The volume of the data on the web is doubled since the emergence of Web 2.0 technologies. The data mining in the user generated entities and extracting the derived knowledge and information patterns is the new threat to privacy of individuals [2]. One of the simplest and most common approaches for collective intelligence is the full-text search methods which allow people to query large data set using some key words. The query results are ranked according to some criteria such as