Published Paper Details:

The emergence and spread of infectious diseases often lead to widespread concern and discussion on social media platforms like Twitter. Understanding public sentiment during outbreaks is crucial for health authorities and policymakers to gauge public perception, address misinformation, and implement appropriate interventions. In the case of the Monkeypox outbreak, the analysis of Twitter data using machine learning techniques for sentiment analysis can provide valuable insights into public sentiment and opinions. This study aims to conduct sentiment analysis on a comprehensive dataset collected from Twitter during the Monkeypox outbreak. By leveraging machine learning algorithms, we seek to classify tweets into different sentiment categories, such as positive, negative, or neutral, to discern the general public opinion regarding the outbreak. The sentiment analysis will involve natural language processing (NLP) techniques to understand the sentiments expressed in tweets and quantify the overall polarity of opinions. The dataset comprises tweerts collected during the outbreak period, containing keywords related to Monkeypox. Leveraging machine learning models like Support Vector Machines (SVM), Naive Bayes, or Recurrent Neural Networks (RNNs), we will preprocess the text data, perform feature extraction, and train the models to classify tweets based on sentiment.Understanding the sentiment of the public towards the Monkeypox outbreak from Twitter data can provide valuable insights into community perceptions, concerns, and the overall impact of the outbreak on social media discourse. The findings of this analysis could assist health authorities in devising targeted communication strategies, addressing public concerns, and managing the outbreak more effectively.