Disaster Tweets

Natural Language Processing with Disaster Tweets using BERT

Introduction (Copied from Kaggle)

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).

But, it’s not always clear whether a person’s words are actually announcing a disaster. Take this example:

The author explicitly uses the word “ABLAZE” but means it metaphorically. This is clear to a human right away, especially with the visual aid. But it’s less clear to a machine.

Hardware

GPU is preferred

Free GPU can be used through Google Colab, by changing runtime type to GPU

Dataset

Dataset can be found from https://www.kaggle.com/competitions/nlp-getting-started/data

This dataset was created by the company figure-eight and originally shared on their ‘Data For Everyone’ website here

Tweet source: https://twitter.com/AnyOtherAnnaK/status/629195955506708480

Architecture of Neural Network

Result

The model was ranked 33th out of a total of 812 teams participating in the Twitter Disaster contest in these two months

Reference

Kaggle. Natural Language Processing with Dis- aster Tweets. Retrieved April 01, 2022 from https://www.kaggle.com/c/nlp-getting-started.

Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. (2018). Improving language understanding by generative pre-training (2018).

Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2020. How to Fine-Tune BERT for Text Classification? arXiv:1905.05583 (2019)