Asian Journal of Computer Science and Technology (AJCST)
Sentiment Analysis on Myocardial Infarction Using Tweets DataAuthor : M. B. Monicka and A. Krishnaveni
Volume 8 No.1 Special Issue:February 2019 pp 10-14
In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.
Opinion Mining, Myocardial Infarction, Machine Learning.
 M.S. Neethu and R. Rajashree, “Sentiment Analysis in Twitter using Machine Learning Techniques” 4th ICCCNT 2013, Tiruchengode, India. IEEE, 31661.
 A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification Using Distant Supervision”, Stanford University, Technical Paper, 2009.
 V. M. K. Peddinti and P. Chintalapoodi, “Domain adaptation in sentiment analysis of twitter,” in Analyzing Microtext Workshop, AAAI, 2011.
 Long-Sheng Chen, Cheng-Hsiang Liu, and Hui-Ju Chiu, “A neural network based approach for sentiment classification in the blogosphere”, Journal of Informetrics, Vol. 5, pp. 313–322, 2011.
 Kennedy and D. Inkpen, “Sentiment classification of movie reviews using contextual valence shifters,” Computational Intelligence, Vol. 22, pp. 110–125, 2006.
 Qingliang Miao, Qiudan Li, and Ruwei Dai, “AMAZING: A sentiment mining and retrieval system”, Expert Systems with Applications, Vol. 36, pp. 7192–7198, 2009.
 B. Pang and L. Lee , “Sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceeding of the 43rd Annual Meeting of the ACL, Stroudsburg, 2004, pp. 221 of 619.
 H. Tang., S. Tan., and Cheng X., “A survey on sentiment detection of reviews,” In Expert Systems with Applications: An International Journal, Vol. 36, pp. 10760-10773, 2009.
 Shailesh Kumar Yadav, Sentiment Analysis and Classification: A Survey, International Journal of Advance Research in Computer Science and Management Studies Vol. 3, No. 3, March 2015.
 Kennedy and D. Inkpen, Sentiment classification of movie reviews using contextual valence shifters, Computational Intelligence, Vol. 22, pp. 110–125, 2006.
 B. Pang, and L. Lee, “Opinion mining and sentiment analysis”, Foundations and Trends® in Information Retrieval, Vol. 2, No. 1–2, pp.1-135, 2008.
 J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, “Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques”, In Proceedings of the Third IEEE International Conference on Data Mining, 2003.
 A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford, Vol.1, pp.12, 2009.
 A. Pak, and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” in LREc, Vol. 10, pp.1320-1326, May 2010.
 M. Bilal, H. Israr, M. Shahid, and A. Khan, “Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques,” Journal of King Saud University-Computer and Information Sciences, Vol. 28, No. 3, pp. 330-344, 2016.
 W. Medhat, A. Hassan and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, Vol. 5, No. 4, pp. 1093-1113, 2014.
 Alexander Pak and Patrick Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, In LREC-2010, Valletta, Malta, 2010.