poltextLAB AI Laboratory
Babel Machine is maintained by poltextLAB Artificial Intelligence Laboratory in Budapest and developed with international collaborators across comparative politics and text-as-data research.
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No-code multilingual AI for comparative research
Political text, decoded.
The Babel Machine turns uploaded text into structured research data: CAP (Comparative Agendas Project) topics, manifesto labels, sentiment, emotions, named entities, ILLFRAMES, ONTOLISST, and entity-targeted sentiment analysis.
14+
coding tasks
100+
supported languages
190+
institutional users worldwide
Demo
Test the classifiers live.
Upload text and explore the Babel Machine classifiers in action.
Classifier Suite
Pick the taxonomy, upload the file, receive coded data.
Every classifier is available from the new homepage, grouped by the research decision the user is making.
Policy Topics
CAP Major (22)
Automated coding of CAP major policy topic codes.
Policy TopicsCAP Minor (214)
Automated coding of detailed CAP minor policy topic codes.
Media CodingCAP Major + Media1
CAP major policy topic codes plus Media1 categories.
Media CodingCAP Major + Media2
CAP major policy topics plus expanded Media2 categories.
Media CodingCAP Minor + Media1
Fine-grained policy topics combined with Media1 codes.
ManifestosManifesto
Automated coding of party manifesto text.
ToneSentiment 3
Three-way sentiment coding for comparative text analysis.
ToneEmotions 6
Automated coding of six emotion categories.
ToneEmotions 9
Automated coding of nine emotion categories.
ToneEmotions 10
Automated coding of ten emotion categories.
FramesILLFRAMES
Automated coding of illiberal policy frames.
FramesONTOLISST
Automated coding of ONTOLISST categories.
ExtractionNER
Named-Entity Recognition for people, organizations, and places.
ExtractionABSA 3
Three-way sentiment analysis targeted at named entities inside sentences.
Coming Soon
Multi-task Upload
Multiple classification tasks with one upload.
Embedded workflow
Test the upload and classification flow directly in the demo section above.
Run demoSupported languages
The platform supports broad multilingual coverage for CAP (Comparative Agendas Project), manifesto, sentiment, emotions, ILLFRAMES, ONTOLISST, ABSA, and NER tools.
See languagesHow to cite
Sebok, M., Mate, A., Ring, O., Kovacs, V., & Lehoczki, R. (2025). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach.
DOIGDPR note
Uploaded files are processed in a secure cloud environment to return coded CSV outputs. Submission data is used only for returning results and identifying the submitting organization.
GDPR referencePlatform updates
Babel Machine continues to expand classifier coverage, language support, and research infrastructure.
Browse toolsGet in touch
Address: 4 Toth Kalman utca, 1097 Budapest, Hungary.
Email us
Language Coverage
Built for multilingual social science, not just English demos.
Current tasks cover 100+ languages, with the broadest coverage for English, Hungarian, French, German, Polish, Czech, Slovak, Spanish, Portuguese, Italian, Dutch, and Danish.
English
Hungarian
French
German
Polish
Czech
Slovak
Spanish
Portuguese
Italian
Dutch
Danish
Norwegian
Romanian
Bulgarian
Serbian
Croatian
Slovenian
Swedish
Finnish
Estonian
Latvian
Lithuanian
Greek
Russian
Hebrew
Armenian
Georgian
Japanese
Korean
Icelandic
Research Backbone
Research-grade AI, wrapped in a no-code workflow.
How to cite
Sebok, M., Mate, A., Ring, O., Kovacs, V., & Lehoczki, R. (2025). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 43(2), 295-317.
Read the articleGDPR-ready processing
Uploaded files are processed to return coded CSV outputs. Personal data associated with submission is used only for returning results and identifying the submitting organization, and users can request deletion at any time.
GDPR referenceInstitutional home
Babel Machine is maintained by poltextLAB Artificial Intelligence Laboratory, a Budapest-based research lab focused on text mining and AI for social science research.
Visit poltextLAB
Users
Institutional customers and the affiliations of personal users
Researchers from 190+ institutions in 40 countries across 5 continents have used the platform and models (May 2026).
Sponsors
Current sponsors and infrastructure partners.
Babel Machine is maintained by poltextLAB Artificial Intelligence Laboratory (ELTE CSS) in Budapest and developed with international collaborators across comparative politics and text-as-data research.
poltextLAB's institutional home
Artificial Intelligence National Laboratory project
Research infrastructure support
Cloud infrastructure support
Research infrastructure partner
V-SHIFT Momentum Project
Miklos Sebok's Excellence project (identifier: 151324), funded by the Hungarian National Research, Development and Innovation Office's National Research Excellence Programme.
Additionally supported by the European Union's Horizon 2020 programme under grant agreement no. 101008468.
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