wri-dssg-omdena/policy-data-analyzer
Building a model to recognize incentives for landscape restoration in environmental policies from Latin America, the US and India. Bringing NLP to the world of policy analysis through an extensible framework that includes scraping, preprocessing, active learning and text analysis pipelines.
This project helps policy analysts rapidly understand regulations related to environmental efforts like forest and landscape restoration. It takes unstructured policy documents and identifies financial and economic incentives mentioned within them. The output is an analysis showing where these incentives are present, designed for government officials, environmental policy experts, and researchers.
No commits in the last 6 months.
Use this if you need to quickly identify specific financial and economic incentives within large sets of policy documents, especially for environmental and restoration initiatives.
Not ideal if your primary goal is general policy sentiment analysis or if your documents are not focused on financial or economic incentives for environmental restoration.
Stars
36
Forks
8
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 01, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/wri-dssg-omdena/policy-data-analyzer"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
IliaZenkov/NLP-keras-nltk-lime
Classification of tweets pertinent to disaster events. NLP basics with a focus on text...
deepmancer/tweet-disaster-detection
fine-tuned BERT and scikit-learn models for real-time classification of disaster-related tweets,...
wang0324/TwitterRelevanceClassification
Classifies if a tweet is relevant to a disaster or not
kushv16/Disaster_Tweets_Analysis
Project based on Natural Language Processing to identify if the given tweet indicates a disaster.
cagandhi/Twitter-Disaster-Prediction
A Bidirectional LSTM model to classify whether a given tweet talks about a real disaster or not....