Predicting Future Products Rate using Machine Learning Algorithms

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Shaimaa Mahmoud 1,* Mahmoud Hussein 1 Arabi Keshk 1

1. Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt

* Corresponding author.


Received: 10 Feb. 2020 / Revised: 4 Mar. 2020 / Accepted: 16 Mar. 2020 / Published: 8 Oct. 2020

Index Terms

Twitter, Sentiment Analysis, Machine Learning, prediction


Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

Cite This Paper

Shaimaa Mahmoud, Mahmoud Hussein, Arabi Keshk, "Predicting Future Products Rate using Machine Learning Algorithms", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.5, pp.41-51, 2020. DOI:10.5815/ijisa.2020.05.04


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