disaster-tweets

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

Natural Language Processing with Disaster Tweets

Kaggle NLP with Disaster Tweets: LSTM classifier that flags tweets announcing real disasters (public score 0.766).

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

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

Classification of Disaster Tweets as REAL or FAKE using Machine Learning

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

Benchmark NLP models on Kaggle “Disaster Tweets”: TF-IDF + Naive Bayes baseline, Keras deep nets (Dense/LSTM/GRU/BiRNN/Conv1D), and TensorFlow Hub Universal Sentence Encoder transfer learning—compared using accuracy, precision, recall, and F1.

Kaggle real or not disaster tweets classification using FastText.

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

Disaster tweet classification using NLP: TF-IDF vs Word Embeddings vs Sentence Transformers

🚀 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

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

Disaster tweet classification: EDA, grouped validation, TF-IDF baselines and frozen MPNet embeddings.

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