In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.
Cox, M. A., & Cox, T. F. (2008). Multidimensional scaling. In Handbook of data visualization. In Handbook of Data Visualization (pp. 315–347). Berlin, Heidelberg: Springer.
D. Tang, F. W. (2014). Learning Sentiment-Specific Word Embedding. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 1555–1565.
Dos Santos, C. N., & Gatti, M. (2014). Deep Convolutional Neural Networks for. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 69–78.
Jifara, W., Jiang, F., Rho, S., Cheng, M., & Liu, S. (2019). Medical image denoising using convolutional neural network: a residual learning approach. The Journal of Supercomputing, 704–718.
Krouska, A., Troussas, C., & Virvou, M. (2016). The effect of preprocessing techniques on Twitter sentiment analysis. 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, 1–6.
Kruskal, J. B. (1964). Nonmetric multidimensional scaling: A numerical approach. Psychometrika.
Kruskal, J. B. (1978). Multidimensional scaling. Sage.
Ramteke, J., Shah, S., Godhia, D., & Shaikh, A. (2016). Election result prediction using Twitter sentiment analysis. 2016 international conference on inventive computation technologies (ICICT), Vol. 1, IEEE, 1–5.
Salminen, J., Yoganathan, V., Corporan, J., Jansen, B. J., & Jung, S.-G. (2019). Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research, 203–217.
Santra, A. K. (2012). Genetic Algorithm and Confusion Matrix for Document Clustering. International Journal of Computer Science Issues (IJCSI), 9(1), 322–328.
Sebastiani, F. (2002). Consiglio Nazionale Delle Ricerche. Machine learning in automated text categorization. ACM Computing Surveys, 34, 1–47.
Shimodaira, H., Noma, K.-I., Nakai, M., & Sagayama, S. (2002). Dynamic Time-Alignment Kernel in Support Vector Machine. Advances in neural information processing systems, 21–928.
Soucy, P. &. (2005, July). Beyond TFIDF weighting for text categorization in the vector space model. IJCAI, 5, 1130–1135.
Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126.
Trsteniak, B., Mikac, S., & Donko, D. (2014). KNN with TF-IDF based Framework for Text Categorization. Procedia Engineering, 69, 1356–1364.
Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. Pacific- -Asia Conference on Knowledge Discovery and Data Mining, 201–213.
Yan, B. Y. (2017). Microblog sentiment classification using parallel SVM in apache spark. 2017 IEEE International Congress on Big Data (BigData Congress), IEEE, 282–288.