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Recently, there are unprecedented data growth originating from differentonline platforms which contribute to big data in terms of volume, velocity,variety and veracity (4Vs). Given this nature of big data which isunstructured, performing analytics to extract meaningful information iscurrently a great challenge to big data analytics. Collecting and analyzingunstructured textual data allows decision makers to study the escalation ofcomments/posts on our social media platforms. Hence, there is need forautomatic big data analysis to overcome the noise and the non-reliability ofthese unstructured dataset from the digital media platforms. However, currentmachine learning algorithms used are performance driven focusing on theclassification/prediction accuracy based on known properties learned from thetraining samples. With the learning task in a large dataset, most machinelearning models are known to require high computational cost which eventuallyleads to computational complexity. In this work, two supervised machinelearning algorithms are combined with text mining techniques to produce ahybrid model which consists of Na\"ive Bayes and support vector machines (SVM).This is to increase the efficiency and accuracy of the results obtained andalso to reduce the computational cost and complexity. The system also providesan open platform where a group of persons with a common interest can sharetheir comments/messages and these comments classified automatically as legal orillegal. This improves the quality of conversation among users. The hybridmodel was developed using WEKA tools and Java programming language. The resultshows that the hybrid model gave 96.76% accuracy as against the 61.45% and69.21% of the Na\"ive Bayes and SVM models respectively.

Text Classification Using Hybrid Machine Learning Algorithms on Big Data