a survey on sentiment study in twitter data using Hadoop streaming API
Keywords:Java-Hadoop, Map-decrease, Opinion Mining, Positive analysis, Twitter-API.
Twitter is an online individual with singular correspondence webpage that conveys created live of knowledge which is handled, by semi-formed and disheveled information. In this work, a system that accomplishes demand of tweets analysis in Twitter-API is talked relating to. to revamp its ability, it is planned to finish the work on the java-Hadoop system, a typically got coursed managing organize utilizing the Map cut back parallel composition purpose of the scan. At long last, wide examinations area unit about to be driven on evident educational gatherings, with a necessity to accomplish in every implies that really matters indefinite or lots of obvious truth than the planned systems in composing. The focus is providing the positive negative and neutral analysis by opinion Mining.
 T. Wilson, J. Wiebe and P. Hoffmann, â€œRecognizing contextual polarity in phrase-level sentiment analysis,â€ in Proceedings of HLT and EMNLP. ACL, (2005), pp. 347â€“354
 C. C. Tao, S. K. Kim, Y. A. Lin, Y. Y. Yu, G. Bradski, A. Y. Ng and Kunle Olukotun, â€œMap-reduce for machine learning on multicoreâ€, In NIPS, vol. 6, (2006), pp. 281-288.
 L. Jimmy, and A. Kolcz, â€œLarge-scale machine learning at twitterâ€, In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, ACM, (2012), pp. 793-804.
 B. Jiang, U. Topaloglu and F. Yu, â€œTowards large-scale twitter mining for drug-related adverse eventsâ€, In Proceedings of the 2012 international workshop on Smart health and wellbeing, ACM, (2012), pp. 25-32.
 L. Bingwei, E. Blasch, Y. Chen, D. Shen and G. Chen, â€œScalable Sentiment Classification for Big Data Analysis Using Naive Bayes Classifierâ€, In Big Data, 2013 IEEE International Conference on, IEEE, (2013), pp. 99-104.
 Ã. Cuesta, David F. and MarÃa D. R-Moreno, â€œA Framework for Massive Twitter Data Extraction and Analysisâ€, In Malaysian Journal of Computer Science, (2014), pp. 50-67.
 S. Michal and A. Romanowski, â€œSentiment analysis of Twitter data within big data distributed environment for stock predictionâ€, In Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on, IEEE, (2015), pp. 1349-1354.
 T. Mohit, I. Gohokar, J. Sable, D. Paratwar and R. Wajgi, â€œMulti-Class Tweet Categorization Using Map Reduce Paradigmâ€, In International Journal of Computer Trends and Technology. (2014), pp. 78-81.
 D. Jeffrey and S. Ghemawat, â€œMapReduce: simplified data processing on large clustersâ€, Communications of the ACM 51.1, (2008), pp. 107-113.
 B. Yingyi, â€œHaLoop: Efficient iterative data processing on large clustersâ€, Proceedings of the VLDB Endowment 3.1-2, (2010), pp. 285-296.
 T. Maite, â€œLexicon-based methods for sentiment analysisâ€, Computational linguistics 37.2, (2011), pp. 267-307.
 R. Tushar and S. Srivastava, â€œAnalyzing stock market movements using twitter sentiment analysisâ€, Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE Computer Society, (2012).
 D. Pessemier and Martens â€œMovieTweetings: A Movie Rating Dataset Collected From Twitterâ€, Ghent University, Ghent, Belgium, (2013).
 Twitter. Twitter Search API, available at https://dev.twitter.com/rest/public/search.
 V. D. Katkar, S. V. Kulkarni, â€œA Novel Parallel implementation of Naive Bayesian classifier for Big Dataâ€, International Conference on Green Computing, Communication and Conservation of Energy, 978-1-4673-6126-2/2013 IEEE, pp. 847-852.
 S. Kumar, F. Morstatter and H. Liu, â€œTwitter Data Analyticsâ€, Springer Science & Business Media, (2013).
 B. Vishal, â€œData Mining in Dynamic Social Networks and Fuzzy Systemsâ€, IGI Global, (2013).
 G. Elmer, G. Langlois and J. Redden, â€œCompromised Data: From Social Media to Big Dataâ€, Bloomsbury Publishing USA, (2015).
 Nalini K. and L. J. Sheela, â€œClassification of Tweets Using Text Classifier to Detect Cyber Bullyingâ€, In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI, Springer International Publishing, vol. 2, (2015), pp. 637-645.
 Jaba S. L. and Dr V. Shanthi, â€œAn Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learningâ€, International Journal of Computer Theory and Engineering, vol. 1, no. 2, pp. 154.
 T. White, â€œHadoop: The Definitive Guideâ€, Third Edition, O'Reilley, (2012).
 L. George, â€œHBase: The Definitive Guideâ€, O'Reilley, (2011).
 E. Hewitt, â€œCassandra: The Definitive Guideâ€, O'Reilley, (2010).
 A. Gates, â€œProgramming Pigâ€, O'Reilley, (2011).