Real or Not? NLP with Disaster Tweets Challenge

Real or Not? NLP with Disaster Tweets Challenge

Predict which Tweets are about real disasters and which ones are not.

Welcome to one of our Getting Started machine learning competitions.

This particular challenge is perfect for data scientists looking to get started with Natural Language Processing. The competition dataset is not too big, and even if you don’t have much personal computing power, you can do all of the work in our free, no-setup, Jupyter Notebooks environment called Kaggle Notebooks.

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.

Awards:-

To really get things started, Google Cloud AutoML Natural Language is awarding $2,000 to each of the top 5 scoring solutions that use only Google Cloud AutoML in a Kaggle Notebook before 3/23/2020 at 11:59pm UTC.

For those who haven’t worked with language models before, AutoML Natural Language can be a great way to get your first model up and running with ease and without spending time on architecture search or hyper-parameter tuning.

This notebook will help you train your first AutoML Natural Language model and submit predictions for the competition. We’re also offering free GCP credits that you can use to train your AutoML models!

Deadline:- 23-03-2020

Take this challenge