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* Add readme * complete steps of the pipeline * Update README.md fix a grammer --------- Co-authored-by: parisa-zahedi <[email protected]> Co-authored-by: parisa-zahedi <[email protected]>
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# re-python-package | ||
# INTEREST | ||
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This template repository is created by the [UU Research Engineering team](https://utrechtuniversity.github.io/research-engineering/) and is aimed to provide a simple project template for python package development. | ||
The code in this repository implements a pipeline to extract specific articles from a large corpus. | ||
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The template includes: | ||
- Project directory structure | ||
- Project configuration using `pyproject.toml` | ||
- GitHub actions workflows for testing, linting, type checking and publishing on pypi | ||
Currently, this tool is tailored for the [Delpher Kranten](https://www.delpher.nl/nl/kranten) corpus, but it can be adapted for other corpora as well. | ||
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Many other project templates exist, check for example this advanced [python template](https://github.com/NLeSC/python-template) by the NL eScience Center. | ||
Articles can be filtered based on individual or multiple features such as title, year, decade, or a set of keywords. To select the most relevant articles, we utilize models such as tf-idf. These models are configurable and extendable. | ||
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## Dependencies | ||
This template uses: | ||
| Tool | Aim | | ||
| --- | --- | | ||
| setuptools | building | | ||
| flake8, pylint | code linting | | ||
| pytest | testing | | ||
| pydocstyle | checking docstrings | | ||
| mypy | type checking | | ||
| sphinx | documentation generation | | ||
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If needed, most of these tools can be removed by simply removing the GitHub action that calls the tool, or by changing `pyproject.toml` | ||
## Getting Started | ||
Clone this repository to your working station to obtain examples and python scripts: | ||
``` | ||
git clone https://github.com/UtrechtUniversity/historical-news-sentiment.git | ||
``` | ||
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## How to use | ||
### Prerequisites | ||
To install and run this project you need to have the following prerequisites installed. | ||
``` | ||
- Python [>=3.9, <3.11] | ||
``` | ||
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### Step 1: Create new repository from this template | ||
Click `Use this template` at the top of this page to create a new repository using this template | ||
### Installation | ||
#### Option 1 - Install interest package | ||
To run the project, ensure to install the interest package that is part of this project. | ||
``` | ||
pip install interest | ||
``` | ||
#### Option 2 - Run from source code | ||
If you want to run the scripts without installation you need to: | ||
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### Step 2: Change the name of your package in pyproject.toml | ||
- Change the name of the folder `package-name` to the name of your package | ||
- Open `pyproject.toml` and change `package-name` to the name of your package | ||
- Also change the authors and optionally any other items that you want to change | ||
- Install requirement | ||
```commandline | ||
pip install setuptools wheel | ||
``` | ||
Change your current working directory to the location of your pyproject.toml file. | ||
``` | ||
python -m build | ||
pip install . | ||
``` | ||
- Set PYTHONPATH environment: | ||
On Linux and Mac OS, you might have to set the PYTHONPATH environment variable to point to this directory. | ||
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### Step 3: Change GitHub Actions workflow | ||
- Open `.github/workflows/python-package.yml` | ||
- Change `package-name` to the name of your package (line 21) | ||
- Many actions are commented out, uncomment them when you want to start using them. | ||
```commandline | ||
export PYTHONPATH="current working directory/historical-news-sentiment:${PYTHONPATH}" | ||
``` | ||
### Built with | ||
These packages are automatically installed in the step above: | ||
* [scikit-learn](https://scikit-learn.org/stable/) | ||
* [SciPy](https://scipy.org) | ||
* [NumPy](https://numpy.org) | ||
* [spaCy](https://spacy.io) | ||
* [pandas](https://pandas.pydata.org) | ||
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### Step 4: Replace this README file with your README | ||
- You may use this [README template](https://github.com/UtrechtUniversity/rse-project-templates/blob/master/README-template.md) | ||
## Usage | ||
### 1. Preparation | ||
#### Data Prepration | ||
Before proceeding, ensure that you have the data prepared in the following format: The expected format is a set of JSON files compressed in the .gz format. Each JSON file contains metadata related to a newsletter, magazine, etc., as well as a list of article titles and their corresponding bodies. These files may be organized within different folders or sub-folders. | ||
Below is a snapshot of the JSON file format: | ||
```commandline | ||
{ | ||
"newsletter_metadata": { | ||
"title": "Newspaper title ..", | ||
"language": "NL", | ||
"date": "1878-04-29", | ||
... | ||
}, | ||
"articles": { | ||
"1": { | ||
"title": "title of article1 ", | ||
"body": [ | ||
"paragraph 1 ....", | ||
"paragraph 2...." | ||
] | ||
}, | ||
"2": { | ||
"title": "title of article2", | ||
"body": [ | ||
"text..." | ||
] | ||
} | ||
} | ||
} | ||
``` | ||
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### Step 5: Change the license file | ||
- Open `LICENSE`, change the copyright holder when required (line 3) | ||
- Or replace the entire license file if another license applies | ||
In our use case, the harvested KB data is in XML format. We have provided the following script to transform the original data into the expected format. | ||
``` | ||
from interest.preprocessor.parser import XMLExtractor | ||
### Step 6: Add a citation file | ||
- Create a citation file for your repository using [cffinit](https://citation-file-format.github.io/cff-initializer-javascript/#/) | ||
extractor = XMLExtractor(Path(input_dir), Path(output_dir)) | ||
extractor.extract_xml_string() | ||
``` | ||
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### Step 7: Publising on Pypi (optional/later) | ||
For publishing the package on Pypi you need to create [API tokens](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries). | ||
Navigate to scripts folder and run: | ||
``` | ||
python3 convert_input_files.py --input_dir path/to/raw/xml/data --output_dir path/to/converted/json/compressed/output | ||
``` | ||
#### Customize input-file | ||
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In order to define a corpus with a new data format you should: | ||
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- add a new input_file_type to [INPUT_FILE_TYPES](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/__init__.py) | ||
- implement a class that inherits from [input_file.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/input_file.py). | ||
This class is customized to read a new data format. In our case-study we defined [delpher_kranten.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/filter/delpher_kranten.py). | ||
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### 2. Filtering | ||
In this step, you may select articles based on a filter or a collection of filters. Articles can be filtered by title, year, decade, or a set of keywords defined in the ```config.json``` file. | ||
```commandline | ||
"filters": [ | ||
{ | ||
"type": "TitleFilter", | ||
"title": "example" | ||
}, | ||
{ | ||
"type": "YearFilter", | ||
"year": 2022 | ||
}, | ||
{ | ||
"type": "DecadeFilter", | ||
"decade": 1960 | ||
}, | ||
{ | ||
"type": "KeywordsFilter", | ||
"keywords": ["sustainability", "green"] | ||
} | ||
] | ||
} | ||
``` | ||
run the following to filter the articles: | ||
```commandline | ||
python3 scripts/step1_filter_articles.py --input-dir "path/to/converted/json/compressed/output/" --output-dir "output_filter/" --input-type "delpher_kranten" --glob "*.gz" | ||
``` | ||
In our case, input-type is "delpher_kranten", and input data is a set of compresed json files with ```.gz``` extension. | ||
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The output of this script is a JSON file for each selected article in the following format: | ||
```commandline | ||
{ | ||
"file_path": "output/transfered_data/00/KRANTEN_KBPERS01_000002100.json.gz", | ||
"article_id": "5", | ||
"Date": "1878-04-29", | ||
"Title": "Opregte Haarlemsche Courant" | ||
} | ||
``` | ||
### 3. Categorization by timestamp | ||
The output files generated in the previous step are categorized based on a specified [period-type](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/interest/temporal_categorization/__init__.py), | ||
such as ```year``` or ```decade```. This categorization is essential for subsequent steps, especially if you intend to apply tf-idf or other models to specific periods. In our case, we applied tf-idf per decade. | ||
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```commandline | ||
python3 scripts/step2_categorize_by_timestamp.py --input-dir "output_filter/" --glob "*.json" --period-type "decade" --output-dir "output_timestamped/" | ||
``` | ||
The output consists of a .csv file for each period, such as one file per decade, containing the ```file_path``` and ```article_id``` of selected articles. | ||
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### 4. Select final articles | ||
This step is applicable when articles are filtered (in step 2) using a set of keywords. | ||
By utilizing tf-idf, the most relevant articles related to the specified topic (defined by the provided keywords) are selected. | ||
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Before applying tf-idf, articles containing any of the specified keywords in their title are selected. | ||
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From the rest of articles, to choose the most relevant ones, you can specify one of the following criteria in [config.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/config.json): | ||
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- Threshold for the tf-idf score value | ||
- Maximum number of selected articles with the top scores | ||
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```commandline | ||
"article_selector": | ||
{ | ||
"type": "threshold", | ||
"value": "0.02" | ||
}, | ||
OR | ||
"article_selector": | ||
{ | ||
"type": "num_articles", | ||
"value": "200" | ||
}, | ||
``` | ||
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The following script, add a new column, ```selected``` to the .csv files from the previous step. | ||
```commandline | ||
python3 scripts/3_select_final_articles.py --input_dir "output/output_timestamped/" | ||
``` | ||
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### 5. Generate output | ||
As the final step of the pipeline, the text of the selected articles is saved in a .csv file, which can be used for manual labeling. The user has the option to choose whether the text should be divided into paragraphs. | ||
This feature can be set in [config.py](https://github.com/UtrechtUniversity/historical-news-sentiment/blob/main/config.json). | ||
```commandline | ||
"output_unit": "paragraph" | ||
OR | ||
"output_unit": "text" | ||
``` | ||
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```commandline | ||
python3 scripts/step4_generate_output.py --input_dir "output/output_timestamped/” --output-dir “output/output_results/“ --glob “*.csv” | ||
``` | ||
## About the Project | ||
**Date**: February 2024 | ||
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**Researcher(s)**: | ||
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Pim Huijnen ([email protected]) | ||
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**Research Software Engineer(s)**: | ||
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- Parisa Zahedi ([email protected]) | ||
- Shiva Nadi ([email protected]) | ||
- Matty Vermet ([email protected]) | ||
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### License | ||
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The code in this project is released under [MIT license](LICENSE). | ||
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## Contributing | ||
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Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. | ||
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To contribute: | ||
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1. Fork the Project | ||
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) | ||
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) | ||
4. Push to the Branch (`git push origin feature/AmazingFeature`) | ||
5. Open a Pull Request | ||
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## Contact | ||
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Pim Huijnen - [email protected] | ||
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Project Link: [https://github.com/UtrechtUniversity/historical-news-sentiment](https://github.com/UtrechtUniversity/historical-news-sentiment) |