It is estimated that 85% of worldwide data is held in unstructured/unlabelled formats - increasing at a rate of roughly 7 million digital pages per day. Exploiting these large datasets can open the door for providing policy makers, corporations, and end-users with unprecedented knowledge for better planning, decision making, and new services. Deep learning and probabilistic topic modeling have shown great potential for analysing such datasets. This analysis helps in: discovering anomalies within these datasets, unravelling underlying patterns/trends, or finding similar texts within a dataset. We'll illustrate how we can use a combined unsupervised deep learning and topic modeling approach for sentiment analysis requiring minimal feature engineering or prior assumptions, and outperforming the state of the art approaches to sentiment analysis.