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Group 2 - twitter-persona (Python) #40
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Package ReviewPlease 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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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.)
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Otherwise, great job! |
Package ReviewPlease 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: 1.5 hrs Review CommentsThe idea of this package is truly exciting, and it is materialized very well in the code. The group has done a great job at collaborating well, which is evident from the package repository. I would also like to list some of my observations and suggestions that might help with some looking into.
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Package ReviewPlease check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
DocumentationThe package includes all the following forms of documentation:
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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.)
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Submitting Author: Renzo Wijngaarden(@RenzoWijn)
All current maintainers: (@Yurui-Feng, @roanraina, @tiger12055)
Package Name: twitter-persona
One-Line Description of Package: A way to quickly determine the sentiment of a given Twitter user.
Repository Link: https://github.com/UBC-MDS/twitter-persona
Version submitted: TBD
Editor: TBD
Reviewer 1: Yingxin Song
Reviewer 2: Qurat-ul-Ain Azim
Reviewer 3: Mengjun Chen
Reviewer 4: Alexander Taciuk
Archive: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD
Description
Scope
Please indicate which category or categories this package falls under:
For all submissions, explain how the and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
The audience for our package is Twitter users who are looking to quickly see what kind of other users they're interacting with.
TweePy is similar to our package, but focuses more on the frequency and analysis of how a user is tweeting. Our solution is low-code and easier to interact with.
@tag
the editor you contacted:NA
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