Sentiment analysis for health crisis management in smart cities

Abstract

Machine learning becomes necessary. consists in creating systems that learn or improve performance according to the data they process. It is a decision-making tool thanks to its predictive power. In our project, we will be focusing on the analysis of of sentiment in social networks, more specifically on the Twitter platform, in the context of the coronavirus pandemic. Our main objective will be to determine the emotional tone of users’ discourse by classifying their messages into three main categories: positive, neutral and negative . We will use machine learning and natural language processing techniques to classify tweets. We will combine Long ShortTerm Memory (LSTM) model with Elephant Herding Optimization (EHO) algorithm, .