Tools for working with word frequencies from various corpora.
Author: Rob Speer
wordfreq requires Python 3 and depends on a few other Python modules (msgpack-python, langcodes, and ftfy). You can install it and its dependencies in the usual way, either by getting it from pip:
pip3 install wordfreq
or by getting the repository and running its setup.py:
python3 setup.py install
To handle word frequency lookups in Japanese, you need to additionally install mecab-python3, which itself depends on libmecab-dev. These commands will install them on Ubuntu:
sudo apt-get install mecab-ipadic-utf8 libmecab-dev pip3 install mecab-python3
## Unicode data
The tokenizers used to split non-Japanese phrases use regexes built using the unicodedata module from Python 3.4, which uses Unicode version 6.3.0. To update these regexes, run scripts/gen_regex.py.
wordfreq is freely redistributable under the MIT license (see MIT-LICENSE.txt), and it includes data files that may be redistributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/).
Ngram Viewer graphs and data may be freely used for any purpose, although acknowledgement of Google Books Ngram Viewer as the source, and inclusion of a link to http://books.google.com/ngrams, would be appreciated.
It also contains data derived from the following Creative Commons-licensed sources:
- The Leeds Internet Corpus, from the University of Leeds Centre for Translation Studies (http://corpus.leeds.ac.uk/list.html)
- The OpenSubtitles Frequency Word Lists, by Invoke IT Limited (https://invokeit.wordpress.com/frequency-word-lists/)
- Wikipedia, the free encyclopedia (http://www.wikipedia.org)
Some additional data was collected by a custom application that watches the streaming Twitter API, in accordance with Twitter’s Developer Agreement & Policy. This software only gives statistics about words that are very commonly used on Twitter; it does not display or republish any Twitter content.