Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy

© 2020 by IJCTT Journal
Volume-68 Issue-10
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.

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Web References
[1] dataset
[2] machine-learning
[3] [4] targets
[6] 01212020-01262020
[7] 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.