A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Last update: Oct 07, 2022

Overview

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).

Coded by Guillaume Chevalier at Neuraxio Inc.

A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Related tags

Overview

Multilingual Latent Dirichlet Allocation (LDA) Pipeline

Usage

How it works

Supported Languages

Dependencies and their license

Unit tests

License

Owner

Artifici Online Services inc.

ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python)

Code for our paper "Mask-Align: Self-Supervised Neural Word Alignment" in ACL 2021

[KBS] Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks

Analyse japanese ebooks using MeCab to determine the difficulty level for japanese learners

Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

Code repository for "It's About Time: Analog clock Reading in the Wild"

基于GRU网络的句子判断程序/A program based on GRU network for judging sentences

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Production First and Production Ready End-to-End Keyword Spotting Toolkit

Anomaly Detection 이상치 탐지 전처리 모듈

BERN2: an advanced neural biomedical namedentity recognition and normalization tool

Athena is an open-source implementation of end-to-end speech processing engine.

Chinese Named Entity Recognization (BiLSTM with PyTorch)

Sequence Modeling with Structured State Spaces

Must-read papers on improving efficiency for pre-trained language models.

Unsupervised Language Modeling at scale for robust sentiment classification

Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

Trained T5 and T5-large model for creating keywords from text

The official code for “DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction”, ACM MM, Oral Paper, 2021.

Sploitus - Command line search tool for sploitus.com. Think searchsploit, but with more POCs