TY - JOUR ID - 65661 TI - The Application of Machine Learning Algorithms for Text Mining based on Sentiment Analysis Approach JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - Samizade, Reza AU - Mahmoudi Saeid Abad, Elnaz AD - Assistant Prof. of Industrial Engineering, Alzahra University, Tehran, Iran AD - MSc. Student of Industrial Engineering, Alzahra University, Tehran, Iran Y1 - 2018 PY - 2018 VL - 10 IS - 2 SP - 309 EP - 330 KW - Naïve bayes KW - neural network KW - Sentiment analysis KW - Support vector machine KW - Text mining DO - 10.22059/jitm.2017.215513.1807 N2 - Classification of the cyber texts and comments into two categories of positive and negative sentiment among social media users is of high importance in the research are related to text mining. In this research, we applied supervised classification methods to classify Persian texts based on sentiment in cyber space. The result of this research is in a form of a system that can decide whether a comment which is published in cyber space such as social networks is considered positive or negative. The comments that are published in Persian movie and movie review websites from 1392 to 1395 are considered as the data set for this research. A part of these data are considered as training and others are considered as testing data. Prior to implementing the algorithms, pre-processing activities such as tokenizing, removing stop words, and n-germs process were applied on the texts. Naïve Bayes, Neural Networks and support vector machine were used for text classification in this study. Out of sample tests showed that there is no evidence indicating that the accuracy of SVM approach is statistically higher than Naïve Bayes or that the accuracy of Naïve Bayes is not statistically higher than NN approach. However, the researchers can conclude that the accuracy of the classification using SVM approach is statistically higher than the accuracy of NN approach in 5% confidence level. UR - https://jitm.ut.ac.ir/article_65661.html L1 - https://jitm.ut.ac.ir/article_65661_c8b4e78bb9acd81d32aaae090dd3eee4.pdf ER -