LukeZhuang/Hierarchical-Attention-Network
Implementation for "Hierarchical Attention Networks for Document Classification"
This project helps sentiment analysts and product managers understand the core sentiment within customer reviews and documents. By analyzing text data, it identifies key sentences and words that contribute most to an overall positive or negative opinion, allowing you to see what drives customer satisfaction or dissatisfaction. It's designed for anyone needing to quickly grasp the crucial parts of lengthy text documents.
No commits in the last 6 months.
Use this if you need to quickly identify the most impactful sentences and words in customer feedback or long-form documents to understand overall sentiment.
Not ideal if you're looking for a simple, out-of-the-box sentiment analysis tool without needing to engage with the underlying model or preprocess data yourself.
Stars
14
Forks
5
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 14, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/LukeZhuang/Hierarchical-Attention-Network"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
kk7nc/HDLTex
HDLTex: Hierarchical Deep Learning for Text Classification
richliao/textClassifier
Text classifier for Hierarchical Attention Networks for Document Classification
RandolphVI/Hierarchical-Multi-Label-Text-Classification
The code of CIKM'19 paper《Hierarchical Multi-label Text Classification: An Attention-based...
yumeng5/LOTClass
[EMNLP 2020] Text Classification Using Label Names Only: A Language Model Self-Training Approach
sgrvinod/a-PyTorch-Tutorial-to-Text-Classification
Hierarchical Attention Networks | a PyTorch Tutorial to Text Classification