readme: ---
  pipeline_tag: text-classification
  language: multilingual
  license: apache-2.0
  tags:
  - "sentiment-analysis"
  - "multilingual"
  widget:
  - text: "I am very happy."
    example_title: "English"
  - text: "Heute bin ich schlecht drauf."
    example_title: "Deutsch"
  - text: "Quel cauchemard!"
    example_title: "Francais"
  - text: "ฉันรักฤดูใบไม้ผลิ"
    example_title: "ภาษาไทย"
  ---
  
  # Multi-lingual sentiment prediction trained from COVID19-related tweets
  
  Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](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](https://huggingface.co/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},
  }
  ```
  
  Enjoy! 
  