Multilingual Latent Dirichlet Allocation (LDA) Pipeline
This project is for text clustering using the Latent Dirichlet Allocation (LDA) algorithm. It can be adapted to many languages provided that the Snowball stemmer, a dependency of this project, supports it.
Usage
from artifici_lda.lda_service import train_lda_pipeline_default
FR_STOPWORDS = [
"le", "les", "la", "un", "de", "en",
"a", "b", "c", "s",
"est", "sur", "tres", "donc", "sont",
# even slang/texto stop words:
"ya", "pis", "yer"]
# Note: this list of stop words is poor and is just as an example.
fr_comments = [
"Un super-chat marche sur le trottoir",
"Les super-chats aiment ronronner",
"Les chats sont ronrons",
"Un super-chien aboie",
"Deux super-chiens",
"Combien de chiens sont en train d'aboyer?"
]
transformed_comments, top_comments, _1_grams, _2_grams = train_lda_pipeline_default(
fr_comments,
n_topics=2,
stopwords=FR_STOPWORDS,
language='french')
print(transformed_comments)
print(top_comments)
print(_1_grams)
print(_2_grams)
Output:
array([[0.14218195, 0.85781805],
[0.11032992, 0.88967008],
[0.16960695, 0.83039305],
[0.88967041, 0.11032959],
[0.8578187 , 0.1421813 ],
[0.83039303, 0.16960697]])
['Un super-chien aboie', 'Les super-chats aiment ronronner']
[[('chiens', 3.4911404011996545), ('super', 2.5000203653313933)],
[('chats', 3.4911393765493255), ('super', 2.499979634668601 )]]
[[('super chiens', 2.4921035508342464)],
[('super chats', 2.492102155345991 )]]
How it works
See Multilingual-LDA-Pipeline-Tutorial for an exhaustive example (intended to be read from top to bottom, not skimmed through). For more explanations on the Inverse Lemmatization, see Stemming-words-from-multiple-languages.
Supported Languages
Those languages are supported:
- Danish
- Dutch
- English
- Finnish
- French
- German
- Hungarian
- Italian
- Norwegian
- Porter
- Portuguese
- Romanian
- Russian
- Spanish
- Swedish
- Turkish
You need to bring your own list of stop words. That could be achieved by computing the Term Frequencies on your corpus (or on a bigger corpus of the same language) and to use some of the most common words as stop words.
Dependencies and their license
numpy==1.14.3 # BSD-3-Clause and BSD-2-Clause BSD-like and Zlib
scikit-learn==0.19.1 # BSD-3-Clause
PyStemmer==1.3.0 # BSD-3-Clause and MIT
snowballstemmer==1.2.1 # BSD-3-Clause and BSD-2-Clause
translitcodec==0.4.0 # MIT License
scipy==1.1.0 # BSD-3-Clause and MIT-like
Unit tests
Run pytest with ./run_tests.sh. Coverage:
----------- coverage: platform linux, python 3.6.7-final-0 -----------
Name Stmts Miss Cover
--------------------------------------------------------------
artifici_lda/__init__.py 0 0 100%
artifici_lda/data_utils.py 39 0 100%
artifici_lda/lda_service.py 31 0 100%
artifici_lda/logic/__init__.py 0 0 100%
artifici_lda/logic/count_vectorizer.py 9 0 100%
artifici_lda/logic/lda.py 23 7 70%
artifici_lda/logic/letter_splitter.py 36 4 89%
artifici_lda/logic/stemmer.py 60 3 95%
artifici_lda/logic/stop_words_remover.py 61 5 92%
--------------------------------------------------------------
TOTAL 259 19 93%
License
This project is published under the MIT License (MIT).
Copyright (c) 2018 Artifici online services inc.
Coded by Guillaume Chevalier at Neuraxio Inc.
21 Sep 25, 2022
18 Nov 28, 2022
3 Jan 14, 2022
3.6k Jan 07, 2023
2.1k Jan 01, 2023
2 Sep 09, 2022
11 Mar 28, 2022
1.3k Dec 30, 2022
9 Oct 11, 2022
40 Nov 25, 2022
838 Dec 19, 2022
6 Oct 18, 2022
158 Dec 27, 2022
212 Nov 21, 2022
7 Apr 01, 2022
1.5k Nov 14, 2022
42 Jul 09, 2022
363 Dec 30, 2022
30 Dec 02, 2022
99 Jan 06, 2023