Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Himanshu Thakur, Aman Kumar Sharma|
|DOI : 10.14445/22312803/IJCTT-V68I10P101|
How to Cite?
Himanshu Thakur, Aman Kumar Sharma, "Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy," International Journal of Computer Trends and Technology, vol. 68, no. 10, pp. 1-8, 2020. Crossref, 10.14445/22312803/IJCTT-V68I10P101
Sentiment Analysis is one of the fastest spreading research fields in computer science, originating it demanding to observe the trace of all the activities in the region. The focus of sentiment analysis is to release data on the features of the author or speaker about an exclusive subject or the total variance of a record next to examine textual data assemble from the countless origin. The indicated paper is conferring an equivalent study to evaluate and formulate a list of three supervised machine learning techniques (Support vector machine, K-Nearest Neighbor, and Random Forest) on the basis of a literature survey that has opted in this research work. To evolve and validate a mechanism to compute better classification accuracy results from among the selected best-supervised machine learning classifiers.
 M. Tsytsarau and T. Palpanas, “Survey on mining subjective data on the web,” Journal of Data Mining and Knowledge Discovery, Vol. 24, No. 3, pp. 478-514, 2012.
 A. Kumar and M.T Sebastian, “Sentiment Analysis: A Perspective on its Past, Present, and Future” International Journal of Intelligent Systems and Applications 4.10, 2012
 B. Liu, “Handbook Chapter: Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing,” Handbook of Natural Language Processing. Marcel Dekker, Inc. New York, NY, USA, 2009.
 K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” in Proceedings of the 12th international conference on World Wide Web, 2003, pp. 519–528.
 T. Wilson, J. Wiebe, and P. Hoffman, "Recognizing Contextual Polarity in phrase-level Sentiment Analysis," in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing,2005.
 L. Yu, J. Wu, P. Chann, and H. Chu, “Using a Contextual Entropy Model to Expand Emotion Words and Their Intensity for the Sentiment Classification ff Stock Market News,” Knowledge-Based System, Vol. 41, pp. 89–97, 2013.
 M. Hagenau, M. Liebmann and D. Neumann, “Automated News Reading: Stock Price Prediction Based on Financial News Using Context-Capturing Features,” Decision Support System, 2013.
 T. Xu, Q. Peng and Y. Cheng, “Identifying the Semantic Orientation of Terms using S-HAL for Sentiment Analysis,” Knowledge-Based System, Vol. 35, pp. 279–89, 2012.
 I. Maks and P. Vossen, “A Lexicon Model for Deep Sentiment Analysis and Opinion Mining Applications,” Decision Support System, Vol. 53, pp. 680–688, 2012
 B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Found Trends Inform Retrieval, Vol. 2, No. 1-2, 2008.
 A. Montoyo, P. Martinez-Barco, and A. Balahur, “Subjectivity and Sentiment Analysis: An Overview of The Current State of The Area and Envisaged Developments,” Decision Support System, Vol. 53, No. 4, 2012.
 R. M. Duwairi and I. Qarqaz, “Arabic Sentiment Analysis using Supervised Classification, Future Internet of Things and Cloud (FiCloud),” in International Conference on. IEEE, 2014.
 G. Vinodhini and R. M. Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey,” International Journal of Computer Applications, pp. 282-292, June 2012.
 T. Nasukawa, “Sentiment Analysis: Capturing Favorability Using Natural Language Processing,” Definition of Sentiment Expressions,” pp. 70–77, 2003.
 X. Ding, S. M. Street, B. Liu, S. M. Street, P. S. Yu, and S. M. Street, “A Holistic Lexicon-Based Approach to Opinion Mining,” ACM, pp. 231–239, 2008.
 van de Camp Matje, van den Bosch Antal, “The socialist network,” Decision Support System, Vol. 53: pp. 761–769, 2012.
 H. Rui, Y. Liu, and A. Whinston, “Whose and What Chatter Matters? The Effect of Tweets on Movie Sales,” Decision Support System, Vol. 55, No. 4, 2013.
 A. Reyes and P. Rosso, “Making Objective Decisions from Subjective Data: Detecting Irony in Customer Reviews,” Decision Support System, Vol. 53, pp. 754–760. 2012.
 T. Cover and P. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transaction Information Theory, Vol. 13, No. 1, pp. 21–27, 1967.
 B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs Up? Sentiment Classification using Machine Learning Techniques,” in Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 2002.
 A. Singh and R. Sathyaraj, “A Comparison between Classification Algorithms on Different Datasets Methodologies Using Rapid Miner,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, May 2016.
 A. Badresiya, S. Vohra and J. Teraiya, “Performance Analysis of Supervised Techniques for Review Spam Detection,” International Journal of Advanced Networking Applications, 2014.
 A. Ghosh, E. F. Fassnacht, P. K. Joshi, and B. Koch, "A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales,” International Journal of Applied Erath Observation and Geoinformation, Vol. 26, pp. 49– 63, 2014.
 L. I. Kuncheva and J. J. Rodriguez, “A weighted voting framework for classifiers ensembles,” Knowledge and Information Systems, Vol. 38, No. 2, pp. 259–275, 2014.
 S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by Latent Semantic Analysis," Journal of the American Society for Information Science, 1990.
 A. D’Andrea, F. Ferri, P.Griffoni, and T. Guzzo, “Approaches, Tools and Applications for Sentiment Analysis Implementation,” International Journal of Computer Applications, Vol. 125, No.3, September 2015.
 https://www.simplilearn.com/what-is-machine-learning-and-why-itmatters- article
 V. Vapnik, “The nature of statistical learning theory. Springer,” New York., 2012.
 E. Fix and J. L. Hodges, “Discriminatory Analysis, Nonparametric Discrimination,” USAF School of Aviation Medicine, Randolph Field, Tex., Project 21-49-004, Rept. 4, February 1951.
 P. N. Tan, M. Steinbach, and V. Kumar, "Introduction to Data Mining,” Pearson Addison-Wesley, 2006.
 Priyanka Namdev, Prof. Lakhan Singh "A Survey of Sentiment Analysis Process and Technologies" International Journal of Engineering Trends and Technology 67.11 (2019):153-156.
 Srinivasan Suresh, "Prediction of Average Annual Daily Traffic Using Machine Learning Methods" SSRG International Journal of Computer Science and Engineering 6.11 (2019): 51-54. Himanshu Thakur et al. / IJCTT, 68(10), 1-8, 2020
 https://www.kaggle.com/jchen2186/machine-learning-withiris- dataset
 https://www.kaggle.com/cdabakoglu/heart-diseaseclassifications- machine-learning
 https://www.kaggle.com/buddhiniw/breast-cancer-prediction  https://www.kaggle.com/prakharrathi25/banking-datasetmarketing- targets
 https://www.kaggle.com/brendaso/2019-coronavirus-dataset- 01212020-01262020
 https://www.kaggle.com/free4ever1/instagram-fake-spammergenuine- accounts
Sentiment Analysis, Sentiment Classification, Opinion Mining, Feature selection, Machine Learning, Supervised Learning, Support Vector Machine, K- Nearest Neighbor, Random forest, Ensemble learning, Jupyter Notebook.