Multi-lingual sentiment prediction trained from COVID19-related tweets

Repository: https://github.com/clampert/multilingual-sentiment-analysis/

Model trained on a large-scale (18437530 examples) dataset of multi-lingual tweets that was collected between March 2020 and November 2021 using Twitter’s Streaming API with varying COVID19-related keywords. Labels were auto-general based on the presence of positive and negative emoticons. For details on the dataset, see our IEEE BigData 2021 publication.

Base model is sentence-transformers/stsb-xlm-r-multilingual. It was finetuned for sequence classification with positive and negative labels for two epochs (48 hours on 8xP100 GPUs).

Citation

If you use our model your work, please cite:

@inproceedings{lampert2021overcoming,
  title={Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis},
  author={Jasmin Lampert and Christoph H. Lampert},
  booktitle={IEEE International Conference on Big Data (BigData)},
  year={2021},
  note={Special Session: Machine Learning on Big Data},
}

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