Bloc n°4 : Analyse prédictive de données non-structurées par l'intelligence artificielle.
- Competition - https://www.kaggle.com/c/nlp-getting-started
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.
- import_dataset.ipynb import tweet dataset and tokenizes it.
- model_prediction.py predict true or false announce of a disaster with a logistic regression.
- import_dataset.ipynb import tweet dataset and encodes it.
- deep_prediction.py predict true or false announce of a disaster with a NLP.