Abstract
The current decade has witnessed a significant amount of research in the field of sentiment analysis (SA). Several applications have emerged to evidence the necessity of research in this area. On the contrary, the size of micro-blogs content is overgrowing and likely to increase even faster shortly. Social media applications have become part and parcel of our daily lives, as they urge the public to express their opinions and share information around the world. Especially during disasters, people are likely to utilize social media to communicate their hindrances. In this article, we investigate the severity of disaster events from micro-blog messages posted by people during natural calamities and emergencies using deep learning techniques. In particular, the work employs a joint model that combines the features of convolutional neural networks (CNN) with recurrent neural networks (RNN), taking account of the coarse-grained local features generated via CNN and long-range dependencies learned through RNN for analysis of small text messages. Furthermore, the proposed model is evaluated for both binary and fine-grained analyses tested over two different datasets. The accuracy of 87% is observed for binary classification and up to 65% for a three-class problem. The intended work finds usefulness in many instants of disaster relief and crisis management.
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Wadawadagi, R., Pagi, V. (2021). Disaster Severity Analysis from Micro-Blog Texts Using Deep-NN. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_14
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