An approach to solve the Kaggle Competition, Natural Language Processing with Disaster Tweets
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Updated
Apr 13, 2023 - Python
An approach to solve the Kaggle Competition, Natural Language Processing with Disaster Tweets
Creating a model for the Kaggle competition: https://www.kaggle.com/competitions/nlp-getting-started/overview
🚀 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
Utilizing Natural Language Processing (NLP) to analyze and classify tweets for detecting disaster-related content.
Classifier for predicting if a tweet is about real disasters or not.
Classification of Disaster Tweets as REAL or FAKE using Machine Learning
(Re) Introduction to Tensorflow Natural Language Processing
A comparative study on different word vectorization techniques and machine learning algorithms on disaster tweets.
My Kaggle submission notebook - 83.8% Accuracy 🤟 (Top 8%)
This project was developed for the Natural Language Processing with Disaster Tweets Kaggle competition
[FIUBA] 75.06/95.58 Organización de Datos 2020 - Trabajo Práctico 2 - Machine Learning
Kaggle real or not disaster tweets classification using FastText.
A Multitask Framework for Present and Absent Keyphrase Generation using Knowledge Graphs
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.
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