International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 2 (2016) pp 1404-1407 © Research India Publications. http://www.ripublication.com 1404 Analysis of real time social tweets for opinion mining B. M. Bandgar Research Scholar, Department of Computer Science, Karpagam University, Coimbatore-641021, Tamil Nadu, India. E-mail: bapuraob@yahoo.com Dr. S. Sheeja Associate Professor, Department of Computer Application, Karpagam University, Coimbatore-641021, Tamil Nadu, India. E-mail: sheejaajize@gmail.com Abstract We developed the indigenous Windows based user friendly application in Java to extract, process and classify the real time social network tweet using unstructured models. The meaningful real time tweets are obtained and the same is used for sentimental analysis. The processed meaningful tweets are classified into three different opinion mining classes positive, negative and neutral by using unstructured algorithms such as EEC, IPC and SWNC model. The SWNC Model gave better results over the EEC and IPC model. Their results are compared using the confusion matrix, precision and accuracy parameters. The results are also visualized using pie graph. Keyword: Real time social tweets, Unstructured algorithms, classification, opinion mining. Introduction The emergence of social media has given web users a venue for expressing and sharing their thoughts and opinions on different topics and events. Twitter, with nearly 600 million users and over 250 million messages per day, has quickly become a gold mine for organizations to monitor their reputation and brands by extracting and analyzing the sentiments of the tweets posted by the public about them, their markets, and competitors. Sentiment analysis over Twitter data and other similar micro-blogs faces several new challenges due to the typical short length and irregular structure of such content. A number of researchers have carried out the items of the related works. Cui, A. et al.[2] showed that sentiment analysis of tweets is a challenging task due to multilingual and informal messages. A. Bifet and E. Frank [3] proposed a data mining technique used for sentiment knowledge in twitter data streams. The proposed algorithm focuses on classification of data streams and performs sentiment analysis in real time. Only tweets containing an emoticon are considered; which is very small sample of the tweet data. A. Bifet, G. Holmes and B. Pfahringer[4] discussed the handling of tweets in real-time. The authors use only positive/ negative classes for sentiment analysis. S. Argamon et al[5] used a supervised learning algorithm for determining complex sentiment-related attributes. These attributes were classified as attitude type and force. A. Nagy and J. Stamberger[6] proposed a technique to efficiently identify the sentiment from disaster micro-blogs. SentiWordNet 3.0 is used for sentiment detection from tweet. The emoticon is also used as the pictorial representation of a facial expression depicting the mood of a person as angry, sad, normal or happy. Limitations of the technique are ability to expand the initial seeds and continuous maintenance of lists. A. Montejo-Raez et al.[7] proposed an unsupervised approach for sentiment polarity detection from twitter tweets. The limitations of the algorithms are handling of negation, manual labeling process for certain tweets and facing flaws in calculation of final polarity score. J. Kim et al.[8] have proposed a collaborative filtering based model for the prediction of sentiment in Twitter. They used Two Twitter datasets for the evaluation of the proposed model. The results showed the effectiveness of the proposed approach for sentiment prediction. R. Machedon, W. Rand, and Y. Joshi [9] classified social tweet messages into three categories; informative, persuasive and transformative. The tweet data was collected only for 65 music bands where each tweets were labeled by human. The results showed that the proposed method were effective only for the ‘informative’ category. A. Balahur,[10] presented the sentiment analysis technique for Twitter data. They have not given the comparison of results with other. Farhan Hassan Khan et. al.[11] tried to improve the accuracy of text classification and resolve the data sparsity issues. They proposed hybrid model Tweet opinion Mining (TOM) system. They further processed the obtained tweets and used for the classification using three different techniques. They classified the tweets into positive, negative or neutral. This showed good accuracy in the opinion mining. The limitations of their model are no information of extraction of tweets and processing method is not clear. The results obtained are given in statistical terms only. They used the integrated system of different tools in which integration is most complex and developed system based on Linux operating system which is not user friendly. Therefore, we developed the user friendly indigenous Windows based application in Java to extract real time tweets, process and classify the opinions of the people all over the global on the particular event and geographical area from the different sources such as twitter website, news websites etc. Method details The twitter4j API version 1.01 downloaded from [12] and created application on the web site [12]. The token and access