Briiick/NLP-disaster-tweets
Exploring BERT with Kaggle disaster tweets dataset.
This project helps quickly sort incoming tweets to identify which ones are genuinely reporting real-world disasters versus those that are not. It takes tweet text as input and classifies each tweet, helping emergency responders or news organizations prioritize information. The primary user would be someone involved in public safety, crisis communication, or real-time news monitoring.
No commits in the last 6 months.
Use this if you need to rapidly distinguish between genuine disaster reports and other types of tweets based on their text content.
Not ideal if you require analysis beyond simple classification, such as identifying the specific type of disaster or its location.
Stars
12
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 09, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Briiick/NLP-disaster-tweets"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
raghakot/keras-text
Text Classification Library in Keras
shibing624/pytextclassifier
pytextclassifier is a toolkit for text classification....
Sshanu/Relation-Classification-using-Bidirectional-LSTM-Tree
TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences...
celtics1863/envtext
中文环境领域文本分析包,纯神经网络架构,支持EnvBert,LSTM,RNN,word2vec等模型,支持自定义模型,下游任务包括分类,回归,多选,情感分析,命名实体识别等,专题包括气候变化文本...
Langboat/Mengzi
Mengzi Pretrained Models