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People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input.

In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.