Análisis de las noticias chilotas.

Vera Sativa 2642f9759e data: +articles 4 months ago
.dvc 566e867169 Chore: Initialize DVC 4 months ago
data 2642f9759e data: +articles 4 months ago
docs 4bf5bc304b initial commit 4 months ago
models 4bf5bc304b initial commit 4 months ago
notebooks 2642f9759e data: +articles 4 months ago
references 4bf5bc304b initial commit 4 months ago
reports c32436c67f wip: topic modeling 4 months ago
src 2642f9759e data: +articles 4 months ago
.dvcignore 566e867169 Chore: Initialize DVC 4 months ago
.gitignore 2642f9759e data: +articles 4 months ago
LICENSE 4bf5bc304b initial commit 4 months ago
Makefile 4bf5bc304b initial commit 4 months ago
README.md adf19ef8fa feat: scrapping el insular 4 months ago
requirements.txt 4bf5bc304b initial commit 4 months ago
setup.py 4bf5bc304b initial commit 4 months ago
test_environment.py 4bf5bc304b initial commit 4 months ago
tox.ini 4bf5bc304b initial commit 4 months ago

Data Pipeline

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README.md

chilonews

Recolección y analisis de noticias chilotas

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 modeling.
│   └── raw            <- The original, immutable data dump.
│
├── data.dvc           <- Data version control file; see dvc.org for details
│    
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is [optional]death_end[/optinal] a number (for ordering),
│                         the first name, and a short `-` delimited description (be thinking in a eventual class), e.g.
│                         `1.0-vera-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
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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

Objetivos

Keywords / grupos de interes:

  • mujer
  • cannabis / marihuana
  • ciencia / tecnología / ingenería / matematicas
  • lesbiana / bisexual / gay / transgenero / transexual / intersexual / queer