Used for Stats Canada https://www.kaggle.com/competitions/nlp-getting-started/rules

Competition Description 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.

In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified. If this is your first time working on an NLP problem, we've created a quick tutorial to get you up and running.

Disclaimer: The dataset for this competition contains text that may be considered profane, vulgar, or offensive.

Acknowledgments 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 Glove documentation: https://nlp.stanford.edu/projects/glove/