Skip to main content

An off-the-rack NLP sentiment classifier- upload your own corpus or use the pre-installed ones

Project description

# empathyMachines > A standalone NLP sentiment classifier you can import as a module

## Purposes

1. Offer a batteries-included NLP classifier you can use either on it’s own, or to make sentiment predictions as part of a broder NLP project (for example, when classifying customer messages, whether the customer is angry or not might help you determine if this is a compensation request, or a request to adjust their address.) 1. Have the entire sentiment prediction process scaffolded so you can feed in your own training corpus, and easily train an NLP sentiment classifier.

## How to use

1. Download the repo from GitHub (pip install coming later) 1. cd into repo, and pip install -r requirements.txt 1. In your Python code, from EmpathyMachines import EmpathyMachines 1. nlp_classifier = EmpathyMachines() 1. nlp_classifier.train(corpus=’Twitter’) 1. nlp_classifier.predict(text_string)

### Corpora included

### Include your own corpus (UNDER CONSTRUCTION)

Feel free to train a classifier on your own corpus!

Two ways to do this: 1. Read in a .csv file with header row containing “sentiment”, “text”, and optionally, “confidence” 1. Pass in an array of Python dictionaries, with attributes for “sentiment”, “text”, and optionally, “confidence”

1. Create a .csv file with the following fields 1. nlp_classifier.train(corpus=’custom’, corpus_path=’path/to/custom/corpus.csv’, analytics_output=False)

Project details


Release history Release notifications

This version
History Node

0.5.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
empathy_machines-0.5.1-py2.py3-none-any.whl (3.8 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Aug 5, 2016
empathy-machines-0.5.1.tar.gz (3.4 MB) Copy SHA256 hash SHA256 Source None Aug 5, 2016

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page