
# 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! 

{"language": "multilingual", "license": "apache-2.0", "tags": ["sentiment-analysis", "multilingual"], "pipeline_tag": "text-classification", "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": "\u0e09\u0e31\u0e19\u0e23\u0e31\u0e01\u0e24\u0e14\u0e39\u0e43\u0e1a\u0e44\u0e21\u0e49\u0e1c\u0e25\u0e34", "example_title": "\u0e20\u0e32\u0e29\u0e32\u0e44\u0e17\u0e22"}]}
transformers pytorch xlm-roberta text-classification sentiment-analysis multilingual license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Multi-lingual sentiment prediction trained from COVID19-related tweets

Repository: URL

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). 

If you use our model your work, please cite:



Enjoy! 

# Multi-lingual sentiment prediction trained from COVID19-related tweets

Repository: URL

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). 

If you use our model your work, please cite:



Enjoy!
TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Multi-lingual sentiment prediction trained from COVID19-related tweets

Repository: URL

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). 

If you use our model your work, please cite:



Enjoy!
passage: TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Multi-lingual sentiment prediction trained from COVID19-related tweets

Repository: URL

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). 

If you use our model your work, please cite:



Enjoy!