Description

People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. Recent years have seen a rise in the frequency of occurrence of this pattern. Twitter is one of the most extensively utilized social media sites, and it is also one of the finest locations to get a sense of how people feel about events that are linked to the Monkey pox sickness. To achieve this goal, we evaluated the performance of two common pretrained machine learning models (Random forest and Decision tree) and compared their accuracy levels for DT with Count Vectorization and RF with Count Vectorization. DT with TF-ID Vectorization and RF with TF-ID. Finally, the system can estimate some performance metrics such as accuracy, precision, recall and f1-score for both algorithms and compare the algorithms based on accuracy in the form of graph. These results are promising, as they show that machine learning techniques could be used for the sentiment analysis for monkey pox. Our algorithm was able to achieve a high level of accuracy in classifying the sentiment analysis of mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.