文章基本信息

标题：Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy
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作者：Himanshu Thakur ; Aman Kumar Sharma
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2020
卷号：68
期号：10
页码：1-8
DOI：10.14445/22312803/IJCTT-V68I10P101
出版社：Seventh Sense Research Group
摘要：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, KNearest 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 bestsupervised machine learning classifiers.
关键词：Sentiment Analysis; Sentiment Classification; Opinion Mining; Feature selection; Machine Learning; Supervised Learning; Support Vector Machine; K- NearestNeighbor; Random forest; Ensemble learning; JupyterNotebook;