Abstract
Text classification is a common task in machine learning. One of the supervised classification algorithm called Random Forest has been generally used for this task. There is a group of parameters in Random Forest classifier which need to be tuned. If proper tuning is performed on these hyperparameters, the classifier will give a better result. This paper proposes a hybrid approach of Random Forest classifier and Grid Search method for customer feedback data analysis. The tuning approach of Grid Search is applied for tuning the hyperparameters of Random Forest classifier. The Random Forest classifier is used for customer feedback data analysis and then the result is compared with the results which get after applying Grid Search method. The proposed approach provided a promising result in customer feedback data analysis. The experiments in this work show that the accuracy of the proposed model to predict the sentiment on customer feedback data is greater than the performance accuracy obtained by the model without applying parameter tuning.
Highlights
The Classification is a text mining tasks in which class of a particular input is identified by using a given set of labelled data
Customer feedback data analysis is performed by using Random Forest classifier with default hyperparameters and achieved 84.53% of accuracy
Sentiment analysis is essential for a business organization to perform decision making
Summary
The Classification is a text mining tasks in which class of a particular input is identified by using a given set of labelled data. Both supervised and unsupervised methods are used for classification. In the first method, learning is done through predefined labelled data. A set of labelled input documents are given to the model by the end-user. The two main categories of supervised learning are parametric and non-parametric classification. If the density function is known, it will be better to use nonparametric classification. Sentiment analysis is the most attractive platforms which make use of the advantages of supervised classification methods
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More From: International Journal of Advanced Computer Science and Applications
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