Open carrieyanyi opened 5 months ago
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
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Estimated hours spent reviewing: 1
Overall, great work guys, your package is super interesting!
Some changes I would consider making are:
docs
badge, but it would be worth also including it in the 'about' section on the homepage of your repo just to make it extra easy to find.Awesome job, keep up the great work!
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1.5
Overall a really nicely put together package and very useful functions for text analysis. The tests were also well documented and very comprehensive - well done! Below I have listed some suggestions for improvement for your consideration.
__init__.py
file to populate the namespace, which can help make the package more user-friendly. Rather than importing each function one by one, it would be great if the user could simply type import wordwright
or from wordwrite import *
clean_text
and counting_sentences()
functions. Where multiple texts/examples were useful was when demonstrating the language_detection()
function (English vs Spanish). Reducing any unnecessary redundancy can helps the reader to pick out the most important pieces of information in order to understand the package's functionality.I would like to reiterate that the package is in great shape, and that my suggestions are more focused around usability and readability, both for the average user as well as for potential contributors. I look forward to your response to my comments/review!
Mona
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1
Overall, this is a nicely designed package that pack several useful functions together into single package. I can definitely see its potential, especially related to NLP applications. The functions are behaving as expected are also well-documented. One thing I like is that the test suite is testing exceptions and edge cases extensively and the package test error returned ffrom the dependency.
However, during the review I noticed several areas that could be improved in terms of the quality of the package:
wordwright.word_frequency.frequent_words
is missing examples in the
docstrings.poetry
is to be used in the future for installing the development build
of this build, consider to use a less generic environment name in the conda
environment.yaml
file; The current environment name 524
is very likely to
clash with other existing environments.CHANGELOG.md
does not match the one showing in the ReadTheDocs site, which
only contain changelog up to v0.1.0.Keep up the good work!
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 45min
Overall, great work and interesting package!
Submitting Author: Yi Yan (@carrieyanyi) All current maintainers: (@yhan178, @jinyz8888, @alexzhang0825) Package Name: Wordwright One-Line Description of Package: A python package for text processing. Repository Link: https://github.com/UBC-MDS/wordwright Version submitted: https://github.com/UBC-MDS/wordwright/releases/tag/2.0.0 Editor: Tiffany Timbers
Reviewer 1: Orix Au Yeung Reviewer 2: Jake Barnabe Reviewer 3: Mona Zhu Reviewer 4: Nicole Bidwell Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD
Code of Conduct & Commitment to Maintain Package
Description
Wordwright focuses on text analysis and processing. It offers a range of functions, from basic text cleaning to more complex analyses such as language detection, word and sentence counting, word frequency summarizing, and keyword searching. This functionality is particularly useful in fields like data analysis, natural language processing, and anywhere textual data needs to be understood or transformed. Functions are designed to be self-explanatory, which is especially beneficial for those new to programming or text processing.
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