You can deploy this model and start earning money today!

jlattman/
EU_sentiment_german_twitter

Name: EU_sentiment_german_twitter
Availability: InStock

Views

No views yet

EU sentiment detection model

This model is trained to classify the sentiment of statements on the European Union. It finetuned a bert-base-german-cased model on 1700 sentences from the German parliament. It is trained to detect policy skepticism, hard EU skepticism, EU praisal, demand for more EU intergration and EU Speech.

Examples:

EU Speech: this is what the #industry always wanted: EU emissions trading. This naturally leads to rising CO2 and electricity exchange prices. The best answer is a further EU renewable energy booster, which has a price-reducing effect. Fossil is expensive, RE is cheap.
More EU: During COVID19 we need more Europe, not less! Let us together make the European anthem a true hymn for solidarity. For the full video and how to join
Policy Skepticism: It is regrettable that the #BVerfG has not found the courage to put a stop to the communitization of liability risks for ailing banks in Europe. #Karlsruhe is letting German savers and taxpayers down! #ECB #Draghi #Euro #Europe
EU praisal: Also at the Observator. Lindenberg we honor the Europe Day of the #EU. A symbol of the community we should all stand for. #EuropeDay
Hard Euroskepticism: larsklingbeil For us as #AfD, #UK belongs to #Europe, even if they are out of the #EU. That is the difference. The manipulative equation of the inter-national socialists of the #SPD et al. with Eu- rope and the EU, THAT is #populism and a deceptive maneuver! I say: #MoreBritainthanFrance

Model Details Finetuned from model: google-bert/bert-based-german-cased
Epochs: 5
F1 Weighted (5 categories): 0.5776

Lattmann, J. (2025, March 17). Detecting EU sentiment in texts: A LLM Machine Learning application for Euroscepticism research. https://doi.org/10.31219/osf.io/mravb_v1

jlattman/EU_sentiment_german_twitter

jlattman/
EU_sentiment_german_twitter