Wayward is a Python package that helps to identify characteristic terms from single documents or groups of documents.
Wayward is a Python package that helps to identify characteristic terms from single documents or groups of documents. It can be used for keyword extraction and several related tasks, and can create efficient sparse representations for classifiers. It was originally created to provide term weights for word clouds.
Rather than use simple term frequency to estimate the importance of words and phrases, it weighs terms by statistical models known as parsimonious language models. These models are good at picking up the terms that distinguish a text document from other documents in a collection.
For this to work, a preferably large amount of documents is needed to serve as a background collection, to compare the documents of interest to. This could be a random sample of newspaper articles, for instance, but for many applications it works better to take a natural collection, such as a periodical publication, and to fit the model for separate parts (e.g. individual issues, or yearly groups of issues).
See the References section for more information about parsimonious language models and their applications.
Wayward does not do visualization of word clouds. For that, you can paste its output into a tool like http://wordle.net or the IBM Word-Cloud Generator.
Either install the latest release from PyPI:
$ pip install wayward
or clone the git repository, and use Poetry to install the package in editable mode:
$ git clone https://github.com/aolieman/wayward.git $ cd wayward/ $ poetry install
>>> quotes = [ ... "Love all, trust a few, Do wrong to none", ... ... ... "A lover's eyes will gaze an eagle blind. " ... "A lover's ear will hear the lowest sound.", ... ] >>> doc_tokens = [ ... re.sub(r"[.,:;!?\"‘’]|'s\b", " ", quote).lower().split() ... for quote in quotes ... ]
The ParsimoniousLM is initialized with all document tokens as a background corpus, and subsequently takes a single document’s tokens as input. Its top() method returns the top terms and their probabilities:
>>> from wayward import ParsimoniousLM >>> plm = ParsimoniousLM(doc_tokens, w=.1) >>> plm.top(10, doc_tokens[-1]) [('lover', 0.1538461408077277), ('will', 0.1538461408077277), ('eyes', 0.0769230704038643), ('gaze', 0.0769230704038643), ('an', 0.0769230704038643), ('eagle', 0.0769230704038643), ('blind', 0.0769230704038643), ('ear', 0.0769230704038643), ('hear', 0.0769230704038643), ('lowest', 0.0769230704038643)]
The SignificantWordsLM is similarly initialized with a background corpus, but subsequently takes a group of document tokens as input. Its group_top method returns the top terms and their probabilities:
>>> from wayward import SignificantWordsLM >>> swlm = SignificantWordsLM(doc_tokens, lambdas=(.7, .1, .2)) >>> swlm.group_top(10, doc_tokens[-2:], fix_lambdas=True) [('much', 0.09077675276900632), ('lover', 0.06298706244865138), ('will', 0.06298706244865138), ('you', 0.04538837638450315), ('your', 0.04538837638450315), ('rhymes', 0.04538837638450315), ('speak', 0.04538837638450315), ('neither', 0.04538837638450315), ('rhyme', 0.04538837638450315), ('nor', 0.04538837638450315)]
See example/dickens.py for a runnable example with more realistic data.
Origin and Relaunch
This package started out as WeighWords, written by Lars Buitinck at the University of Amsterdam. It provides an efficient parsimonious LM implementation, and a very accessible API.
A recent innovation in language modeling, Significant Words Language Models, led to the addition of a two-way parsimonious language model to this package. This new version targets python 3.x, and after a long slumber deserved a fresh name. The name “Wayward” was chosen because it is a near-homophone of WeighWords, and as a nod to parsimonious language modeling: it uncovers which terms “depart” most from the background collection. The parsimonization algorithm discounts terms that are already well explained by the background model, until the most wayward terms come out on top.
See the Changelog for an overview of the most important changes.
D. Hiemstra, S. Robertson, and H. Zaragoza (2004). Parsimonious Language Models for Information Retrieval. Proc. SIGIR’04.
R. Kaptein, D. Hiemstra, and J. Kamps (2010). How different are Language Models and word clouds?. Proc. ECIR’10.
M. Dehghani, H. Azarbonyad, J. Kamps, D. Hiemstra, and M. Marx (2016). Luhn Revisited: Significant Words Language Models. Proc. CKIM’16.
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