tweepyclean (Python)

[calsvein]

Submitting Author: Nash Makhija (@nashmakh), Matt (@MattTPin), Syad Khan (@syadk), Cal Schafer (@calsvein) Package Name: tweepyclean One-Line Description of Package: ad-on functions to the tweepy package for twitter data processing, word counts and sentiment analysis Repository Link: https://github.com/UBC-MDS/tweepyclean Version submitted: https://github.com/UBC-MDS/tweepyclean/tree/0.3 Editor: Tiffany Timbers (@ttimbers ) Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

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Description

• Include a brief paragraph describing what your package does:

tweepyclean is a Python package built to act as a processor of data generated by the existing Tweepy package that can produce clean data frames, summarize data, and generate new features.

Tweepy is a package built around Twitter's API and is used to scrape tweet information from their servers.

Our package creates functions to process the raw data from Tweepy into a more understandable format by extracting and organizing the contents of tweets for a user. tweepyclean is specifically built to be used in analysis of a specific user's timeline (generated using tweepy's api.user_timeline function). Users can visualize average engagement based on time of day posted, see basic summary statistics of word contents and sentiment analysis of tweets and have a processed dataset for usage in machine learning models.

Scope

• Please indicate which category or categories this package falls under:
  • [ ] Data retrieval
  • [x ] Data extraction
  • [ ] Data munging
  • [ ] Data deposition
  • [ ] Reproducibility
  • [ ] Geospatial
  • [ ] Education
  • [ ] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.

• Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

The tweepy package extracts tweet data, but it is not in a format that it is ready for analysis. Tweepyclean performs functions to convert data into a clean dataframe, performs feature engineering, and creates summary statistics and basic visualizations.

• Who is the target audience and what are scientific applications of this package?

The audience is strictly intended for those who are already using the tweepy package and have a Twitter API key.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Not that we are aware of.

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• [x ] does not violate the Terms of Service of any service it interacts with.
• [ x] has an OSI approved license.
• [x ] contains a README with instructions for installing the development version.
• [x ] includes documentation with examples for all functions.
• [x ] contains a vignette with examples of its essential functions and uses.
• [ x] has a test suite.
• [x ] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

• [ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

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• [x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

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[calsvein]

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