A Review on Social Media and Deep Neural Networks for Sentiment Analysis in Emergency Management

Disaster management is essential to ensuring public safety and reducing the impact of disasters. In recent years, disaster response and recovery have benefited from the use of social media and deep neural network technologies. Examples of sentiment analysis include tweets and deep neural networks. Information about disasters can be found on Twitter in real time. Information, media, and ideas are shared through these channels. Monitoring and evaluating Twitter messages can help emergency management professionals assess the situation, identify affected places, and establish the requirements of the people. Text sentiment analysis is useful in emergency management. Deep neural networks, a type of machine learning, can analyse and categorise people's feelings on social media. By being trained on massive volumes of historical data, deep neural networks can classify tweets as positive, negative, or neutral and produce a sentiment score. Monitoring social media and evaluating sentiment is only one of the many ways in which deep neural networks aid disaster management authorities. The precision of sentiment analysis can also be impacted by sarcasm, ambiguity, and subtleties in language use. In conclusion, disaster management can benefit from the use of Twitter and deep neural networks for sentiment analysis. By monitoring and assessing social media content, authorities can improve situational awareness, make educated judgements, and safeguard affected communities during disasters.