An off-the-rack NLP sentiment classifier- upload your own corpus or use the pre-installed ones
# empathyMachines > A standalone NLP sentiment classifier you can import as a module
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)