The CAP Babel Machine

Introduction

The CAP Babel Project uses the Comparative Agendas Project policy codebook’s major topics to identify the policy areas of texts. In the near future, minor topic coding service will be also available.

The codes assigned are unequivocal, mutually exclusive, and cover all potential policy issues. The codebook distinguishes 21 major policy areas: macroeconomics, civil rights, health, agriculture, labour, education, environment, energy, immigration, transportation, law and crime, social welfare, housing, domestic commerce, defence, technology, foreign trade, international affairs, government operations, public lands and culture.

The 10 CAP domains we are using:

1. Media
2. Social media
3. Parliamentary speech (oral questions, interpellations, bill debates, other plenary speeches)
4. Legislative (bills, laws, motions, legislative decrees, hearings)
5. Executive speech
6. Executive orders
7. Party manifestos
8. Judiciary
9. Budget
10. Public opinion

Differences compared to the CAP website domains:

• we added "social media"
• separated "Parliamentary & Legislative" into two different domains
• separated "Prime Minister & Executive" into two different domains

Current (08/30/2023) data per domain:

Most of the language models that the CAP Babel Machine uses were fine-tuned on training data containing the label 'None' in addition to the 21 CAP major policy topics, indicating that the given text contains no relevant policy content. We use the label 999 for these cases. Note that some of the models (e.g., Danish legislative, Dutch media) do not recognize this category and thus cannot predict if the row has no policy content.

You can upload your datasets here for automated CAP-coding. If you wish to submit multiple datasets one after another, please wait 5-10 minutes between each of your submissions. There are two possibilities for upload: pre-coded datasets or non-coded datasets. The explanation of the form and the dataset requirement is available here.

The upload requires to fill the following form on metadata regarding the dataset. We kindly ask you to upload your dataset, and in case of a pre-coded dataset, if available, please attach the codebook used besides the dataset.

The non-coded datasets should contain an id and a text column. The column names must be in row 1. You are free to add supplementary variables to the dataset beyond the compulsory ones in the columns following them.

Pre-coded datasets must contain the following columns: id, year, major_topic, text. The column names must be in row 1. Uploading a pre-coded sample is optional, but it can help us with calculating performance metrics and fine-tuning the language model behind CAP Babel Machine. The detailed rules of validations are available here. The mandatory data format of major_topic is numeric. All textual CAP categories must be converted to the appropriate numeric code before uploading. Furthermore, records with no policy content should be coded with 999. You are free to add supplementary variables to the dataset beyond the compulsory ones in the columns following them. Automatic processing requires to follow these rules.

After you upload your dataset and your file is successfully processed, you will receive the CAP-coded dataset and a file that includes the three highest probability category predictions by the CAP Babel model and the corresponding probability (softmax) scores assigned to each label. Please be aware that interpreting softmax scores as absolute model confidences could lead to false assumptions about model performance.
See this paper on softmax scores:
Pearce, Tim, et al. (2021, June 9). Understanding Softmax Confidence and Uncertainty. arXiv:2106.04972. arXiv.
https://doi.org/10.48550/arXiv.2106.04972.

If the files you would like to upload are bigger than 100MB, please reach out to us with the download link attached (such as Dropbox or Google Drive) using our contact form. The non-coded datasets should contain an id and a text column. The column names must be in row 1. You are free to add supplementary variables to the dataset beyond the compulsory ones in the columns following them. All datasets must be uploaded in a .csv file format with UTF-8 encoding. If you have any questions or feedback regarding the CAP Babel Machine, please let us know using our contact form. Please keep in mind that we can only get back to you on Hungarian business days.

Submit a dataset:

Name*

E-mail address*

Please use a valid e-mail address.

Institution name*

Dataset name*

Dataset language* -- Please Choose One -- Assamese Azerbaijani Basque Belarusian Bengali Bengali Romanize Bosnian Breton Bulgarian Burmese Burmese zawgyi font Catalan Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Esperanto Estonian Filipino Finnish French Galician Georgian German Greek Gujarati Hausa Hebrew Hindi Hindi Romanize Hungarian Icelandic Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Kurdish (Kurmanji) Kyrgyz Lao Latin Latvian Lithuanian Macedonian Malagasy Malay Malayalam Marathi Mongolian Nepali Norwegian Oriya Oromo Pashto Persian Polish Portuguese Punjabi Romanian Russian Sanskrit Scottish Gaelic Serbian Sindhi Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tamil Tamil Romanize Telugu Telugu Romanize Thai Turkish Ukrainian Urdu Urdu Romanize Uyghur Uzbek Vietnamese Welsh Western Frisian Xhosa Yiddish Other

Please specify:

You selected a language for which limited or no training data is available. The prediction results, therefore, may be worse than for those with extensive training data (Hungarian, English, Italian, Dutch, Spanish, French, and Danish).

Domain* -- Please Choose One -- Media Social media Parliamentary speech (oral questions, interpellations, bill debates, other plenary speeches) Legislative (bills, laws, motions, legislative decrees, hearings) Executive speech Executive orders Party manifestos Judiciary Budget Public opinion

You selected a language and domain combination for which limited or no training data is available. The prediction results, therefore, may be worse than for those with extensive training data.

Unit of observation* -- Please Choose One -- Quasi sentence Sentence Paragraph Full text Budget item

Period (from)*

Period (to)*

Level of dataset* -- Please Choose One -- Supranational/International State Substate

Geographical unit*

Use case* -- Please Choose One -- Research Commercial use Personal use Other

Please specify:

Description

Export file format Excel CSV

• Dataset:
• Choose file

• Pre-coded dataset:
• Choose file

• Codebook:
• Choose file

I have read the instructions and rules.

By ticking the checkbox I declare that this data does not contain any personal data and I am responsible for the legitimacy of the origin of the data. The uploaded data is suitable for text mining and I voluntarily provided the uploaded file(s) to poltextLAB. I also grant permission to poltextLAB researchers to make the data visualisation of the data I uploaded publicly available. In addition, I agree that the uploaded data may be stored on servers within the European Union and be used for data analysis and training purposes without time limit.

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The research was supported by the Ministry of Innovation and Technology NRDI Office and the European Union, in the framework of the RRF-2.3.1-21-2022-00004 Artificial Intelligence National Laboratory project.