kwx 0.1.5.1

BERT, LDA, and TFIDF based keyword extraction in Python

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BERT, LDA, and TFIDF based keyword extraction in Python

Jump to: Models • Usage • Visuals • To-Do

kwx is a toolkit for multilingual keyword extraction based on Google's BERT and Latent Dirichlet Allocation. The package provides a suite of methods to process texts of any language to varying degrees and then extract and analyze keywords from the created corpus (see kwx.languages for the various degrees of language support). A unique focus is allowing users to decide which words to not include in outputs, thereby allowing them to use their own intuitions to fine tune the modeling process.

For a thorough overview of the process and techniques see the Google slides, and reference the documentation for explanations of the models and visualization methods.

Installation via PyPi

kwx can be downloaded from pypi via pip or sourced directly from this repository:

pip install kwx

git clone https://github.com/andrewtavis/kwx.git
cd kwx
python setup.py install

import kwx

Models ↩

Implemented NLP modeling methods within kwx.model include:

BERT

Bidirectional Encoder Representations from Transformers derives representations of words based on nlp models ran over open-source Wikipedia data. These representations are then leveraged to derive corpus topics.

kwx uses sentence-transformers pretrained models. See their GitHub and documentation for the available models.

LDA

Latent Dirichlet Allocation is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. In the case of kwx, documents or text entries are posited to be a mixture of a given number of topics, and the presence of each word in a text body comes from its relation to these derived topics.

Although not as statistically strong as the following machine learning models, LDA provides quick results that are suitable for many applications.

LDA with BERT embeddings

The combination of LDA with BERT via kwx.autoencoder.

Other Methods

The user can also choose to simply query the most common words from a text corpus or compute TFIDF (Term Frequency Inverse Document Frequency) keywords - those that are unique in a text body in comparison to another that's compared. The former method is used in kwx as a baseline to check model efficacy, and the latter is a useful baseline when a user has another text or text body to compare the target corpus against.

Usage ↩

Keyword extraction can be useful to analyze surveys, tweets and other kinds of social media posts, research papers, and further classes of texts. examples.kw_extraction provides an example of how to use kwx by deriving keywords from tweets in the Kaggle Twitter US Airline Sentiment dataset.

The following outlines using kwx to derive keywords from a text corpus with prompt_remove_words as True (the user will be asked if some of the extracted words need to be replaced):

Text Cleaning

from kwx.utils import prepare_data

input_language = "english" # see kwx.languages for options
num_keywords = 15
num_topics = 10
ignore_words = ["words", "user", "knows", "they", "don't", "want"]

# kwx.utils.clean() can be used on a list of lists
text_corpus = prepare_data(
    data="df_or_csv_xlsx_path",
    target_cols="cols_where_texts_are",
    input_language=input_language,
    min_token_freq=0,  # for BERT
    min_token_len=0,  # for BERT
    remove_stopwords=False,  # for BERT
    verbose=True,
)

Keyword Extraction

from kwx.model import extract_kws

# Remove n-grams for BERT training
corpus_no_ngrams = [
    " ".join([t for t in text.split(" ") if "_" not in t]) for text in text_corpus
]

# We can pass keywords for sentence_transformers.SentenceTransformer.encode,
# gensim.models.ldamulticore.LdaMulticore, or sklearn.feature_extraction.text.TfidfVectorizer
bert_kws = extract_kws(
    method="BERT", # "BERT", "LDA_BERT", "LDA", "TFIDF", "frequency"
    bert_st_model="xlm-r-bert-base-nli-stsb-mean-tokens",
    text_corpus=corpus_no_ngrams,  # automatically tokenized if using LDA
    input_language=input_language,
    output_language=None,  # allows the output to be translated
    num_keywords=num_keywords,
    num_topics=num_topics,
    corpuses_to_compare=None,  # for TFIDF
    ignore_words=ignore_words,
    prompt_remove_words=True,  # check words with user
    batch_size=32,
)

The BERT keywords are:

['time', 'flight', 'plane', 'southwestair', 'ticket', 'cancel', 'united', 'baggage',
'love', 'virginamerica', 'service', 'customer', 'delay', 'late', 'hour']

Are there words that should be removed [y/n]? y
Type or copy word(s) to be removed: southwestair, united, virginamerica

The new BERT keywords are:

['late', 'baggage', 'service', 'flight', 'time', 'love', 'book', 'customer',
'response', 'hold', 'hour', 'cancel', 'cancelled_flighted', 'delay', 'plane']

Are there words that should be removed [y/n]? n

The model will be re-ran until all words known to be unreasonable are removed for a suitable output. kwx.model.gen_files could also be used as a run-all function that produces a directory with a keyword text file and visuals (for experienced users wanting quick results).

Visuals ↩

kwx.visuals includes the following functions for presenting and analyzing the results of keyword extraction:

Topic Number Evaluation

A graph of topic coherence and overlap given a variable number of topics to derive keywords from.

from kwx.visuals import graph_topic_num_evals
import matplotlib.pyplot as plt

graph_topic_num_evals(
    method=["lda", "bert", "lda_bert"],
    text_corpus=text_corpus,
    num_keywords=num_keywords,
    topic_nums_to_compare=list(range(5, 15)),
    metrics=True, #  stability and coherence
)
plt.show()

t-SNE

t-SNE allows the user to visualize their topic distribution in both two and three dimensions. Currently available just for LDA, this technique provides another check for model suitability.

from kwx.visuals import t_sne
import matplotlib.pyplot as plt

t_sne(
    dimension="both",  # 2d and 3d are options
    text_corpus=text_corpus,
    num_topics=10,
    remove_3d_outliers=True,
)
plt.show()

pyLDAvis

pyLDAvis is included so that users can inspect LDA extracted topics, and further so that it can easily be generated for output files.

from kwx.visuals import pyLDAvis_topics

pyLDAvis_topics(
    method="lda",
    text_corpus=text_corpus,
    num_topics=10,
    display_ipython=False,  # For Jupyter integration
)

Word Cloud

Word clouds via wordcloud are included for a basic representation of the text corpus - specifically being a way to convey basic visual information to potential stakeholders. The following figure from examples.kw_extraction shows a word cloud generated from tweets of US air carrier passengers:

from kwx.visuals import gen_word_cloud

ignore_words = ["words", "user", "knows", "they", "don't", "want"]

gen_word_cloud(
    text_corpus=text_corpus,
    ignore_words=None,
    height=500,
)

To-Do ↩

• Including more methods to extract keywords
• Allowing key phrase extraction
• Adding t-SNE and pyLDAvis style visualizations for BERT models
• Updates to kwx.languages as lemmatization and other linguistic package dependencies evolve
• Creating, improving and sharing examples
• Improving tests for greater code coverage
• Updating and refining the documentation