Analyse geotagged tweets with LanguageTool to explore the relation between socioeconomic status and the use of standard language.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modelling.
│ └── raw <- The original, immutable data dump.
│
├── scripts <- Scripts to send to a cluster e.g.
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── environment.yml <- The conda environment file for reproducing the analysis environment, e.g.
│ generated with `conda env export -f environment.yml`
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├── ses_ling <- Source code for use in this project.
│ ├── __init__.py <- Makes the project a Python module
│ │
│ ├── data <- Data processing code
│ │ └── make_dataset.py
│ │
│ ├── utils <- Code for any utilities
│ │
│ └── visualization <- Code to create exploratory and results oriented visualizations
│ └── visualize.py
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└── setup.py <- makes project pip installable (pip install -e .) so ses_ling can be imported
Project based on a fork of the cookiecutter data science project template. #cookiecutterdatascience