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--- |
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pipeline_tag: text-classification |
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language: multilingual |
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license: apache-2.0 |
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tags: |
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- "sentiment-analysis" |
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- "multilingual" |
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widget: |
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- text: "I am very happy." |
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example_title: "English" |
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- text: "Heute bin ich schlecht drauf." |
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example_title: "Deutsch" |
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- text: "Quel cauchemard!" |
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example_title: "Francais" |
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- text: "ฉันรักฤดูใบไม้ผลิ" |
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example_title: "ภาษาไทย" |
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--- |
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# Multi-lingual sentiment prediction trained from COVID19-related tweets |
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Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](https://github.com/clampert/multilingual-sentiment-analysis/) |
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Model trained on a large-scale (18437530 examples) dataset of |
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multi-lingual tweets that was collected between March 2020 |
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and November 2021 using Twitter’s Streaming API with varying |
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COVID19-related keywords. Labels were auto-general based on |
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the presence of positive and negative emoticons. For details |
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on the dataset, see our IEEE BigData 2021 publication. |
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Base model is [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual). |
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It was finetuned for sequence classification with `positive` |
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and `negative` labels for two epochs (48 hours on 8xP100 GPUs). |
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## Citation |
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If you use our model your work, please cite: |
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``` |
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@inproceedings{lampert2021overcoming, |
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title={Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis}, |
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author={Jasmin Lampert and Christoph H. Lampert}, |
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booktitle={IEEE International Conference on Big Data (BigData)}, |
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year={2021}, |
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note={Special Session: Machine Learning on Big Data}, |
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} |
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``` |
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Enjoy! |
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