  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.
