In this article, we propose a new Support Vector Machine (SVM) training algorithm based on distributed MapReduce technique. In literature, there are a lots of research that shows us SVM has highest generalization property among classification algorithms used in machine learning area. Also, SVM classifier model is not affected by correlations of the features. But SVM uses quadratic optimization techniques in its training phase. The SVM algorithm is formulated as quadratic optimization problem. Quadratic optimization problem has $O(m^3)$ time and $O(m^2)$ space complexity, where m is the training set size. The computation time of SVM training is quadratic in the number of training instances. In this reason, SVM is not a suitable classification algorithm for large scale dataset classification. To solve this training problem we developed a new distributed MapReduce method developed. Accordingly, (i) SVM algorithm is trained in distributed dataset individually; (ii) then merge all support vectors of classifier model in every trained node; and (iii) iterate these two steps until the classifier model converges to the optimal classifier function. In the implementation phase, large scale social media dataset is presented in TFxIDF matrix. The matrix is used for sentiment analysis to get polarization value. Two and three class models are created for classification method. Confusion matrices of each classification model are presented in tables. Social media messages corpus consists of 108 public and 66 private universities messages in Turkey. Twitter is used for source of corpus. Twitter user messages are collected using Twitter Streaming API. Results are shown in graphics and tables.
Enhancing Stance Classification on Social Media Using Quantified Moral Foundations
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington（印第安纳大学伯明顿分校勒迪信息学、计算与工程学院）
