https://github.com/climbsrocks/empythy

Automated NLP sentiment predictions- batteries included, or use your own data
https://github.com/climbsrocks/empythy

automated-machine-learning batteries-included machine-learning machinelearning nlp nlp-library nlp-machine-learning nlp-sentiment-classifier sentiment sentiment-classification sentiment-classifier sentiment-predictions

Last synced: 4 months ago
JSON representation

Automated NLP sentiment predictions- batteries included, or use your own data

• Host: GitHub
• URL: https://github.com/climbsrocks/empythy
• Owner: ClimbsRocks
• License: mit
• Created: 2016-08-08T18:59:49.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-12-15T10:15:39.000Z (about 7 years ago)
• Last Synced: 2024-10-04T13:33:22.949Z (5 months ago)
• Topics: automated-machine-learning, batteries-included, machine-learning, machinelearning, nlp, nlp-library, nlp-machine-learning, nlp-sentiment-classifier, sentiment, sentiment-classification, sentiment-classifier, sentiment-predictions
• Language: Python
• Homepage: http://empythy.com
• Size: 2.43 MB
• Stars: 18
• Watchers: 3
• Forks: 8
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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. `pip install empythy`

1.

```

from empythy import EmpathyMachines

nlp_classifier = EmpathyMachines()

nlp_classifier.train()

nlp_classifier.predict(text_string)

```

### Corpora included

#### NLTK Movie Reviews

The classic sentiment corpus, 2000 movie reviews already gathered by NLTK.

#### Assembling a custom Twitter sentiment corpus

[CrowdFlower](http://www.crowdflower.com/data-for-everyone) hosts a number of Twitter corpora that have already been graded for sentiment by panels of humans.

I aggregated together 6 of their corpora into a single, aggregated and cleaned corpus, with consistent scoring labels across the entire corpus. The cleaned corpus contains over 45,000 documents, with positive, negative, and neutral sentiments.

### Train on your own corpus

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"

    - Pass the name of the .csv file to train, like so:

    - `nlp_classifier.train(corpus='custom', corpus_path='path/to/custom/corpus.csv')`

1. Pass in an array of Python dictionaries, where each dictionary has attributes for "sentiment", "text", and optionally, "confidence"

    - `nlp_classifier.train(corpus='custom', corpus_array=my_array_of_texts)`

    - Two important parts to this, both `corpus='custom'`, and `corpus_array=my_variable_holding_the_documents`.

### Advanced Usage

1. `nlp_classifier.train(verbose=False)` to turn off print status statements while training.

1. `nlp_classifier.train(print_analytics_results=True)` to print out results of training the classifier.