A hybrid deep learning approach for detecting sentiment polarities and knowledge graph representation on Monkeypox tweets

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. With the help of knowledge graphs, it is possible to extract organized knowledge from texts and images to aid in semantic analysis. 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 Monkeypox sickness. This is because tweets on Twitter are shortened and often updated, both of which contribute to the platform's character. The fundamental objective of this study is to get a deeper comprehension of the diverse range of reactions people have in response to the presence of this condition. This study focuses on determining what individuals think about monkeypox illnesses, presenting a hybrid technique based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). We have considered all three possible polarities of a user's tweet: positive, negative, and neutral. Knowledge graphs are embedded in various healthcare applications to provide improved data representation and knowledge inference, and they have been shown to be helpful in healthcare analytics. We describe in this study a knowledge graph of related events based on Twitter data, which provides a real-time and eventful source of new information. The recommended model's accuracy was 94% on the monkeypox tweet dataset. Other performance metrics such as accuracy, recall, and F1-score were utilized to test our models and results in the most time and resource-effective manner. The findings are then compared to more traditional approaches to machine learning. In addition, the ability to recognize semantic information has been built into the use of knowledge graphs. The findings of this research contribute to an increased awareness of monkeypox infection in the general population.