Polarization Measurement of High Dimensional Social Media Messages With Support Vector Machine Algorithm Using Mapreduce

TL;DR

This paper introduces a distributed MapReduce-based SVM training algorithm to efficiently classify large-scale social media messages for polarization analysis, overcoming traditional SVM computational limitations.

Contribution

The paper presents a novel distributed MapReduce approach for training SVMs on large datasets, enabling scalable polarization measurement of social media messages.

Findings

Effective SVM training on large social media datasets

High classification accuracy demonstrated with Twitter data

Scalable method reduces training time for big data

Abstract

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.