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## Dataset Description
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- **Homepage:**
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- **
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- **Paper:**
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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ReLi
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The dataset contains reviews in Brazilian Portuguese on books written by seven authors: Stephenie Meyer, Thalita Rebouças, Sidney Sheldon, Jorge Amado, George Orwell, José Saramago, and J.D. Salinger. The language used in the reviews varies from highly informal, with slang, abbreviations, neologisms, and emoticons, to more formal reviews with a more elaborate vocabulary.
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### Supported Tasks and Leaderboards
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- `sentiment-analysis`: The dataset can be used to train a model for sentiment analysis, which consists of classifying the sentiment expressed in a sentence as positive, negative, neutral, or mixed. Success on this task is typically measured by achieving a high [F1 score](https://huggingface.co/metrics/f1).
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If you use this dataset in your work, please cite the following publication:\
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```bibtex
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@
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title={
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author={Freitas,
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}
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```
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## Dataset Description
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- **Homepage:** [Corpus ReLi - Linguateca](https://linguateca.pt/Repositorio/ReLi/)
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- **Paper:** [Sparkling Vampire... lol! Annotating Opinions in a Book Review Corpus](https://www.linguateca.pt/Repositorio/ReLi/Anais_ELC2012_Freitasetal.pdf)
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- **Point of Contact:**
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### Dataset Summary
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ReLi is a dataset created by Cláudia Freitas within the framework of the project "Semantic Annotators based on Active Learning" at PUC-Rio. It consists of 1,600 book reviews manually annotated for the presence of opinions on the reviewed book and its polarity.
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The dataset contains reviews in Brazilian Portuguese on books written by seven authors: Stephenie Meyer, Thalita Rebouças, Sidney Sheldon, Jorge Amado, George Orwell, José Saramago, and J.D. Salinger. The language used in the reviews varies from highly informal, with slang, abbreviations, neologisms, and emoticons, to more formal reviews with a more elaborate vocabulary.
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ReLi-SA is an adaptation of the original ReLi dataset for the sentiment analysis task. We attribute a sentiment polarity to each sentence according to the sentiment annotations of its individual tokens.
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### Supported Tasks and Leaderboards
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- `sentiment-analysis`: The dataset can be used to train a model for sentiment analysis, which consists of classifying the sentiment expressed in a sentence as positive, negative, neutral, or mixed. Success on this task is typically measured by achieving a high [F1 score](https://huggingface.co/metrics/f1).
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If you use this dataset in your work, please cite the following publication:\
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```bibtex
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@incollection{freitas2014sparkling,
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title={Sparkling Vampire... lol! Annotating Opinions in a Book Review Corpus},
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author={Freitas, Cl{\'a}udia and Motta, Eduardo and Milidi{\'u}, Ruy Luiz and C{\'e}sar, Juliana},
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booktitle={New Language Technologies and Linguistic Research: A Two-Way Road},
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editor={Alu{\'\i}sio, Sandra and Tagnin, Stella E. O.},
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year={2014},
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publisher={Cambridge Scholars Publishing},
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pages={128--146}
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}
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```
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