ses-ling

Analyse geotagged tweets with LanguageTool to explore the relation between socioeconomic status and the use of standard language.

Project Organization

├── 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

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Project based on a fork of the cookiecutter data science project template. #cookiecutterdatascience