Towards Useful Event Detection and Sentiment Analysis of OSN for Disaster Management

Event detection and sentiment analysis have received considerable research attention due primarily to the very large amount of contextual data and information provided by online social networks (OSN) about every area of life. Real-time events reported in posted messages from OSN during disasters can help in disaster management by providing useful information. In addition, sentiment and emotion analysis can provide pertinent information about the degree of satisfaction with the services provided. However, event detection faces several challenges, including event boundary detection, ambiguity and context sensitivity. Hence, we proposed an LSTM network to handle long sequences and effectively capture temporal dependencies. For this task, the results show that the proposed model achieves a success accuracy greater than 71.7% on a sub-dataset containing events generated from 20 keywords. For the sentiment analysis task, we use bag of words (BoW) representation and the RoBERTa pretrained model. The proposed systems were used on a large dataset with five types of disasters.