| # German sentiment BERT finetuned on news data | |
| Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration. | |
| This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sentiment development in German news articles about migration between 2007 and 2019. | |
| Code for inference (predicting sentiment polarity) on raw text can be found at https://github.com/text-analytics-20/news-sentiment-development/blob/main/sentiment_analysis/bert.py | |
| If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's `germansentiment` package as follows: | |
| First install the package from PyPI: | |
| ```bash | |
| pip install germansentiment | |
| ``` | |
| Then you can use the model in Python: | |
| ```python | |
| from germansentiment import SentimentModel | |
| model = SentimentModel('mdraw/german-news-sentiment-bert') | |
| # Examples from our validation dataset | |
| texts = [ | |
| '[...], schwärmt der parteilose Vizebürgermeister und Historiker Christian Matzka von der "tollen Helferszene".', | |
| 'Flüchtlingsheim 11.05 Uhr: Massenschlägerei', | |
| 'Rotterdam habe einen Migrantenanteil von mehr als 50 Prozent.', | |
| ] | |
| result = model.predict_sentiment(texts) | |
| print(result) | |
| ``` | |
| The code above will print: | |
| ```python | |
| ['positive', 'negative', 'neutral'] | |
| ``` | |