Classification and Prediction of Opinion Mining in Social Networks Data
Mohamed, S., Hussien, M., Keshk, A. (2020). Classification and Prediction of Opinion Mining in Social Networks Data. EKB Journal Management System, 7(1), 32-41. doi: 10.21608/ijci.2020.26841.1015
Shaimaa Mahmoud Mohamed; Mahmoud Hussien; Arabi Keshk. "Classification and Prediction of Opinion Mining in Social Networks Data". EKB Journal Management System, 7, 1, 2020, 32-41. doi: 10.21608/ijci.2020.26841.1015
Mohamed, S., Hussien, M., Keshk, A. (2020). 'Classification and Prediction of Opinion Mining in Social Networks Data', EKB Journal Management System, 7(1), pp. 32-41. doi: 10.21608/ijci.2020.26841.1015
Mohamed, S., Hussien, M., Keshk, A. Classification and Prediction of Opinion Mining in Social Networks Data. EKB Journal Management System, 2020; 7(1): 32-41. doi: 10.21608/ijci.2020.26841.1015

Classification and Prediction of Opinion Mining in Social Networks Data

IJCI. International Journal of Computers and Information
Article 3, Volume 7, Issue 1, 2020, Page 32-41  XML PDF (537.66 K)
Document Type: Original Article
DOI: 10.21608/ijci.2020.26841.1015
View on SCiNiTO View on SCiNiTO
Authors
Shaimaa Mahmoud Mohamed email 1; Mahmoud Hussienorcid 2; Arabi Keshk3
1Computer Science Department, Faculty of Computers and Information,Menoufia University, Shebin Elkom 32511, Egypt
2Faculty of Computers and Information, Menofia University, Egypt
3Faculty of Computer and Information Menoufia University
Abstract
opinion mining in social networks data considers one of the most significant and challenging tasks in our days due to the huge number of information that distributed each day. We can profit from these opinions by utilizing two significant procedures (classification and prediction). Although there is many researchers’ work at this point, it still needs improvement. Therefore, in this paper, we present a method to improve the accuracy of both processes. The improvement is done through cleaning the data set by converting all words to lower case, removing usernames, mentions, links, repeated characters, numbers, delete more than two spaces between words, empty tweets, punctuations and stop words, and converting all words like “isn't” to “is not”. we using both unigrams and bigrams as features. Our data set contains the user's feelings about distributed products, tweets labeled positive or negative, and each product rate from one to five. We implemented this work using different supervised machine learning algorithms like Naïve Bayes, Support Vector Machine and MaxEntropy for the classification process, and Random Forest Regression, Logistic Regression, and Support Vector Regression for the prediction process. At last, we have accuracy in both processes better than existing works. In classification, we achieved an accuracy of 90% and in the prediction process, Support Vector Regression model is able to predict future product rate with a Mean Squared Error (MSE) of 0.4122, Logistic Regression model is able to predict with MSE of 0.4986 and Random Forest Regression model able to predict with MSE of 0.4770.
Keywords
Twitter; Sentiment Analysis; Machine Learning; Classification; and Prediction
Statistics
Article View: 364
PDF Download: 321