International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 12, No. 5, 8 Oct. 2020
Cover page and Table of Contents: PDF (size: 795KB)
Predicting Future Products Rate using Machine Learning Algorithms
Full Text (PDF, 795KB), PP.41-51
Views: 0 Downloads: 0
Author(s)
Index Terms
Twitter, Sentiment Analysis, Machine Learning, prediction
Abstract
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
Reference
[1]Gutiérrez, G., et al. "Analyzing Students Reviews of Teacher Performance Using Support Vector Machines by a Proposed Model." International Symposium on Intelligent Computing Systems. Springer, Cham, 2018.
[2]Ortigosa, Alvaro, José M. Martín, and Rosa M. Carro. "Sentiment analysis in Facebook and its application to e-learning." Computers in human behavior 31 (2014): 527-541.
[3] Esparza, Guadalupe Gutiérrez, et al. "A sentiment analysis model to analyze students reviews of teacher performance using support vector machines." International Symposium on Distributed Computing and Artificial Intelligence. Springer, Cham, 2017.
[4]Kaewyong, Phuripoj, et al. "The possibility of students’ comments automatic interpret using lexicon based sentiment analysis to teacher evaluation." 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015). 2015.
[5]Sarlan, Aliza, Chayanit Nadam, and Shuib Basri. "Twitter sentiment analysis." Proceedings of the 6th International conference on Information Technology and Multimedia. IEEE, 2014.
[6]Wi, D. A. V. I. D. "Applied logistic regression." (2000).
[7]Palmer, David S., et al. "Random forest models to predict aqueous solubility." Journal of chemical information and modeling 47.1 (2007): 150-158.
[8]Suthaharan, Shan. "Support vector machine." Machine learning models and algorithms for big data classification. Springer, Boston, MA, 2016. 207-235.
[9]“Support Vector Machines.” An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, by Nello Cristianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, pp. 93–124.
[10]Oliveira, Nelson, et al. "Retweet Predictive Model for Predicting the Popularity of Tweets." International Conference on Soft Computing and Pattern Recognition. Springer, Cham, 2018.
[11]Vakali, Athena, Nikolaos Kitmeridis, and Maria Panourgia. "A distributed framework for early trending topics detection on big social networks data threads." INNS Conference on Big Data. Springer, Cham, 2016.
[12]Zarrinkalam, Fattane, et al. "Predicting users’ future interests on Twitter." European Conference on Information Retrieval. Springer, Cham, 2017.
[13]Westreich, Daniel, Justin Lessler, and Michele Jonsson Funk. "Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression." Journal of clinical epidemiology 63.8 (2010): 826-833.
[14]Marouli, Georgios. "Comparison between Maximum Entropy and Naïve Bayes classifiers: Case study; Appliance of Machine Learning Algorithms to an Odesk’s Corporation Dataset." (2014).
[15]Maetschke, Stefan R., et al. "Supervised, semi-supervised and unsupervised inference of gene regulatory networks." Briefings in bioinformatics 15.2 (2014): 195-211.