language:ind license:mit sentiment-analysis region:us
The Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.
For each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.
The objectivity score can be calculated as: ObjScore = 1 - (PosScore + NegScore).
@inproceedings{baccianella-etal-2010-sentiwordnet,
    title = "{S}enti{W}ord{N}et 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining",
    author = "Baccianella, Stefano  and
      Esuli, Andrea  and
      Sebastiani, Fabrizio",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf",
    abstract = "In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a lexical resource publicly available for research purposes, now currently licensed to more than 300 research groups and used in a variety of research projects worldwide. Both SENTIWORDNET 1.0 and 3.0 are the result of automatically annotating all WORDNET synsets according to their degrees of positivity, negativity, and neutrality. SENTIWORDNET 1.0 and 3.0 differ (a) in the versions of WORDNET which they annotate (WORDNET 2.0 and 3.0, respectively), (b) in the algorithm used for automatically annotating WORDNET, which now includes (additionally to the previous semi-supervised learning step) a random-walk step for refining the scores. We here discuss SENTIWORDNET 3.0, especially focussing on the improvements concerning aspect (b) that it embodies with respect to version 1.0. We also report the results of evaluating SENTIWORDNET 3.0 against a fragment of WORDNET 3.0 manually annotated for positivity, negativity, and neutrality; these results indicate accuracy improvements of about 20{\%} with respect to SENTIWORDNET 1.0.",
}

@misc{moeljadi_2016,
    title={Neocl/Barasa: Indonesian SentiWordNet},
    url={https://github.com/neocl/barasa},
    journal={GitHub},
    author={Moeljadi, David},
    year={2016}, month={Mar}
}
---
license: mit
tags:
- sentiment-analysis
language:
- ind
---

# barasa

The Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.

For each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.

The objectivity score can be calculated as: ObjScore = 1 - (PosScore + NegScore).

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@inproceedings{baccianella-etal-2010-sentiwordnet,
    title = "{S}enti{W}ord{N}et 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining",
    author = "Baccianella, Stefano  and
      Esuli, Andrea  and
      Sebastiani, Fabrizio",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf",
    abstract = "In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a lexical resource publicly available for research purposes, now currently licensed to more than 300 research groups and used in a variety of research projects worldwide. Both SENTIWORDNET 1.0 and 3.0 are the result of automatically annotating all WORDNET synsets according to their degrees of positivity, negativity, and neutrality. SENTIWORDNET 1.0 and 3.0 differ (a) in the versions of WORDNET which they annotate (WORDNET 2.0 and 3.0, respectively), (b) in the algorithm used for automatically annotating WORDNET, which now includes (additionally to the previous semi-supervised learning step) a random-walk step for refining the scores. We here discuss SENTIWORDNET 3.0, especially focussing on the improvements concerning aspect (b) that it embodies with respect to version 1.0. We also report the results of evaluating SENTIWORDNET 3.0 against a fragment of WORDNET 3.0 manually annotated for positivity, negativity, and neutrality; these results indicate accuracy improvements of about 20{\%} with respect to SENTIWORDNET 1.0.",
}

@misc{moeljadi_2016,
    title={Neocl/Barasa: Indonesian SentiWordNet},
    url={https://github.com/neocl/barasa},
    journal={GitHub},
    author={Moeljadi, David},
    year={2016}, month={Mar}
}
```

## License

MIT

## Homepage

[https://github.com/neocl/barasa](https://github.com/neocl/barasa)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)