  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 Na\"ive 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 Na\"ive Bayes and SVM models respectively.
