text-features

A library for processing text features in a dataframe.

Dependencies :globe_with_meridians:

Python 3.11.6:

• pandas
• pyarrow
• sentence_transformers
• tqdm
• nltk
• textblob
• numpy

Raison D'être :thought_balloon:

textfeats transforms text into values that can be used by a model.

Architecture :triangular_ruler:

textfeats is a functional library, meaning that each phase of feature extraction gets put through a different function until the final output. The features its computes are as follows:

1. Embeddings - A mean of all the embeddings from the text values in the row.
2. Stemmer - A count of the keywords containing the stem from the text values in the row.
3. Sentiment - A measure of the positive / negative sentiment from the text values in the row.
4. Objectivity - A measure of the objectivity / subjectivity from the text values in the row.
5. Count - The number of words from the text values in the row.

Installation :inbox_tray:

This is a python package hosted on pypi, so to install simply run the following command:

pip install textfeats

or install using this local repository:

python setup.py install --old-and-unmanageable

Usage example :eyes:

The use of textfeatures is entirely through code due to it being a library. It attempts to hide most of its complexity from the user, so it only has a few functions of relevance in its outward API.

Generating Features

To generate features:

import datetime

import pandas as pd

from textfeats.process import process

df = ... # Your timeseries dataframe
df = process(df, True, {"injuries"})

This will produce a dataframe that contains the new text related features.

License :memo:

The project is available under the MIT License.