Text Classification Using Hybrid Machine Learning Algorithms on Big Data

Abstract

Recently, there are unprecedented data growth originating from different online platforms which contribute to big data in terms of volume, velocity, variety and veracity (4Vs). Given this nature of big data which is unstructured, performing analytics to extract meaningful information is currently a great challenge to big data analytics. Collecting and analyzing unstructured textual data allows decision makers to study the escalation of comments/posts on our social media platforms. Hence, there is need for automatic big data analysis to overcome the noise and the non-reliability of these unstructured dataset from the digital media platforms. However, current machine learning algorithms used are performance driven focusing on the classification/prediction accuracy based on known properties learned from the training samples. With the learning task in a large dataset, most machine learning models are known to require high computational cost which eventually leads to computational complexity. In this work, two supervised machine learning algorithms are combined with text mining techniques to produce a hybrid model which consists of Naïve Bayes and support vector machines (SVM). This is to increase the efficiency and accuracy of the results obtained and also to reduce the computational cost and complexity. The system also provides an open platform where a group of persons with a common interest can share their comments/messages and these comments classified automatically as legal or illegal. This improves the quality of conversation among users. The hybrid model was developed using WEKA tools and Java programming language. The result shows that the hybrid model gave 96.76% accuracy as against the 61.45% and 69.21% of the Naïve Bayes and SVM models respectively.