In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an rganization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.

Botnet is considered a multifunctional malware. It can be leveraged by criminals to launch variety of malware attacks such as click fraud, DDOS, spam, etc. Moreover, the botnets pretend the normal traffic by leveraging common protocols such as IRC, HTTP, DNS and P2P for command control. Therefore, distinguishing botnet behavior is challenging because it has similarities with normal protocols behaviors. Most of previous researches focus on detecting specific type of botnet. Moreover, they rely on limited number of features. In addition, they do not select the optimal model by tuning the hyperparameters of machine learning algorithms. In this paper we use a recent dataset that containing a diverse set of botnet traces and wider flow features. We select the relevant features using several ranking algorithms. Eventually, the optimal models are selected by tuning the hyperparameters of machine learning algorithms.