  In recent days, the amount of Cyber Security text data shared via social
media resources mainly Twitter has increased. An accurate analysis of this data
can help to develop cyber threat situational awareness framework for a cyber
threat. This work proposes a deep learning based approach for tweet data
analysis. To convert the tweets into numerical representations, various text
representations are employed. These features are feed into deep learning
architecture for optimal feature extraction as well as classification. Various
hyperparameter tuning approaches are used for identifying optimal text
representation method as well as optimal network parameters and network
structures for deep learning models. For comparative analysis, the classical
text representation method with classical machine learning algorithm is
employed. From the detailed analysis of experiments, we found that the deep
learning architecture with advanced text representation methods performed
better than the classical text representation and classical machine learning
algorithms. The primary reason for this is that the advanced text
representation methods have the capability to learn sequential properties which
exist among the textual data and deep learning architectures learns the optimal
features along with decreasing the feature size.
