IDRISI is the largest-scale publicly-available Twitter Location Mention Prediction (LMP) datasets, in both English and Arabic languages. It contains 41 disaster events of different types (e.g., floods, fires). Annotations include tagged LMs in posts, location types (e.g., cities, streets), links to OSM toponyms, & usefulness of features for LMD.

A Multitask Framework for Present and Absent Keyphrase Generation using Knowledge Graphs

My Kaggle submission notebook - 83.8% Accuracy 🤟 (Top 8%)

An approach to solve the Kaggle Competition, Natural Language Processing with Disaster Tweets

Kaggle real or not disaster tweets classification using FastText.

Utilizing Natural Language Processing (NLP) to analyze and classify tweets for detecting disaster-related content.

Classification of Disaster Tweets as REAL or FAKE using Machine Learning

🚀 Welcome to my Kaggle submission for "Natural Language Processing with Disaster Tweets." In this challenge, we explore tweets, using NLP to distinguish between those about real disasters and those that aren't. The goal is to build a robust model for accurate disaster-related tweet prediction. 🏆 Impressive F1 score of 0.79926 on the public leader

[FIUBA] 75.06/95.58 Organización de Datos 2020 - Trabajo Práctico 2 - Machine Learning

Creating a model for the Kaggle competition: https://www.kaggle.com/competitions/nlp-getting-started/overview

Classifier for predicting if a tweet is about real disasters or not.

Natural Language Processing with Disaster Tweets

(Re) Introduction to Tensorflow Natural Language Processing

A comparative study on different word vectorization techniques and machine learning algorithms on disaster tweets.

This project was developed for the Natural Language Processing with Disaster Tweets Kaggle competition