A Statistical approach to evaluate the efficiency and effectiveness of the Machine Learning algorithms analyzing Sentiments

Kumar, Archana P and Nayak, Ashalatha and Shenoy, Manjula K (2019) A Statistical approach to evaluate the efficiency and effectiveness of the Machine Learning algorithms analyzing Sentiments. In: IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, 11/08/2019, Manipal Institute of Technology.

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Abstract

In the process of analyzing sentiments for a given dataset various machine learning techniques are used. The models using these learning algorithms help in determining the sentiments across the textual documents. There is a need to evaluate the effectiveness of the models in terms of analyzing and predicting sentiments. This paper provides a statistical approach to measure the effectiveness of the models and also evaluates their effectiveness with respect to the data representations. Here an experimental research is carried out with an inductive mode to measure and evaluate the models. The models are built using Decision Tree, Naive Bayes and Support Vector Machines. Data has been represented using features of Term Frequency and Inverse Document Frequency and Bag-ofwords. Statistical tools used for measuring the models are Chisquare test and Analysis of Variance

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: —Machine Learning, Sentiments, Statistical approach
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 13 Sep 2019 09:14
Last Modified: 13 Sep 2019 09:14
URI: http://eprints.manipal.edu/id/eprint/154527

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