https://github.com/danzek/email-formality-detection

Determine whether an email is formally or informally written
https://github.com/danzek/email-formality-detection

Last synced: about 1 year ago
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Determine whether an email is formally or informally written

• Host: GitHub
• URL: https://github.com/danzek/email-formality-detection
• Owner: danzek
• License: mit
• Created: 2014-11-12T21:07:41.000Z (over 11 years ago)
• Default Branch: mysql
• Last Pushed: 2018-01-19T21:41:18.000Z (over 8 years ago)
• Last Synced: 2025-04-13T16:09:47.746Z (about 1 year ago)
• Language: Python
• Homepage:
• Size: 6.18 MB
• Stars: 3
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# email-formality-detection [![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)

Predict whether an email is formally or informally written. `data/prep_corpus.py` writes the emails to a SQLite

database. `process_corpus.py` creates a text file with extracted features in LibSVM format (for supervised machine

learning). `data/classifier_app` is a web application for framework validation and simplifying manual classification 

of each email as either formal or informal.

This project uses the Enron email corpus, retrieved 

from [https://www.cs.cmu.edu/~./enron/](https://www.cs.cmu.edu/~./enron/). This is a group project for CNIT499NLT 

Natural Language Technologies at Purdue University. The course instructor is Dr. Julia Taylor. 

## Group Members

 - Dan O'Day

 - Robert Hinh

 - Upasita Jain

 - Sangmi Shin

 - Penghao Wang

## License

[MIT](https://github.com/danzek/email-formality-detection/blob/master/LICENSE)

## Database

The `mysql` branch is the main branch with all of the features and up-to-date code. However, the `master` branch uses sqlite rather than MySQL, and is thus easier to run and develop locally. Initial development took place with sqlite, but eventually moved to MySQL since that is the most efficient supported database type on PythonAnywhere hosting service.

## How to Test A Single Feature

Begin inside the directory where you've extracted this project. Using an interactive Python shell (symbolized by `>>>` below—**do not retype the prompt symbols**), do the following:

    >>> from data.models import Corpus

    

Next import your feature(s):

    >>> from features.myfeaturefile import myfeature

Now instantiate the corpus and select a specific email by its ID number:

    >>> c = Corpus()

    >>> email_generator_object = c.fetch_all_emails(column="id", query="120000", exact_match=True)

    >>> email = next(email_generator_object)

Now you have the email object with the specified id. You can pass it to your previously imported feature by using the feature function name:

    >>> myfeature(email)

The feature should return the specified metric.