Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.
peer review Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method
Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method
Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method
turnitin Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method
[1] R. Passonneau, “Sentiment Analysis of Twitter Data,” no. June, pp. 30–38, 2011. [2] M. Ronen Feldman, Bar-Ilan University, Israel , James Sanger, ABS Ventures, Boston, The Text Mining Handbook. Cambridge University Press, 2006. [3] S. Alotaibi, “Sentiment Analysis Challenges of Informal Arabic Language,” vol. 8, no. 2, pp. 278–284, 2017. [4] M. A. Ibrahim and N. Salim, “Sentiment Analysis of Arabic Tweets : With Special Reference Restaurant Tweets,” vol. 4, no. 3, pp. 173–179, 2016. [5] P. Tripathi, S. K. Vishwakarma, and A. Lala, “Sentiment Analysis of English Tweets Using RapidMiner,” 2015. [6] J. Ahmed, “Sentiment Analysis and Classification of Tweets Using Data Mining,” pp. 4–7, 2017. [7] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” no. December, 2013. [8] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” vol. 2, no. 1, 2008. [9] M. Singh, “Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews,” vol. 144, no. 2, pp. 16–19, 2016. [10] A. Bifet and E. Frank, “Sentiment Knowledge Discovery in Twitter Streaming Data.” [11] J. Isabella, “Analysis and evaluation of Feature selectors in opinion mining,” vol. 3, no. 6, pp. 757–762, 2013. [12] D. Liliya and K. Irina, “Improving the Classification Quality of the SVM Classifier for the Imbalanced Datasets on the Base of Ideas the SMOTE Algorithm,” vol. 02002, pp. 8–11, 2017. [13] J. Mathew, M. Luo, C. K. Pang, and H. L. Chan, “Kernel-Based SMOTE for SVM Classification of Imbalanced Datasets,” pp. 1127–1132, 2015. [14] W. Gata, “Gataframework,” gataframework, 2017. [Online]. Available: http://www.gataframework.com/textmining/.