language:jav language:ind sentiment-analysis machine-translation region:us
Sentiment analysis and machine translation data for Javanese and Indonesian.
@article{Tho_2021,
  doi = {10.1088/1742-6596/1869/1/012084},
  url = {https://doi.org/10.1088/1742-6596/1869/1/012084},
  year = 2021,
  month = {apr},
  publisher = {{IOP} Publishing},
  volume = {1869},
  number = {1},
  pages = {012084},
  author = {C Tho and Y Heryadi and L Lukas and A Wibowo},
  title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach},
  journal = {Journal of Physics: Conference Series},
  abstract = {Nowadays mixing one language with another language either in
  spoken or written communication has become a common practice for bilingual
  speakers in daily conversation as well as in social media. Lexicon based
  approach is one of the approaches in extracting the sentiment analysis. This
  study is aimed to compare two lexicon models which are SentiNetWord and VADER
  in extracting the polarity of the code-mixed sentences in Indonesian language
  and Javanese language. 3,963 tweets were gathered from two accounts that
  provide code-mixed tweets. Pre-processing such as removing duplicates,
  translating to English, filter special characters, transform lower case and
  filter stop words were conducted on the tweets. Positive and negative word
  score from lexicon model was then calculated using simple mathematic formula
  in order to classify the polarity. By comparing with the manual labelling,
  the result showed that SentiNetWord perform better than VADER in negative
  sentiments. However, both of the lexicon model did not perform well in
  neutral and positive sentiments. On overall performance, VADER showed better
  performance than SentiNetWord. This study showed that the reason for the
  misclassified was that most of Indonesian language and Javanese language
  consist of words that were considered as positive in both Lexicon model.}
}
---
tags:
- sentiment-analysis
- machine-translation
language:
- jav
- ind
---

# code_mixed_jv_id

Sentiment analysis and machine translation data for Javanese and Indonesian.

## Dataset Usage

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

## Citation

```
@article{Tho_2021,
  doi = {10.1088/1742-6596/1869/1/012084},
  url = {https://doi.org/10.1088/1742-6596/1869/1/012084},
  year = 2021,
  month = {apr},
  publisher = {{IOP} Publishing},
  volume = {1869},
  number = {1},
  pages = {012084},
  author = {C Tho and Y Heryadi and L Lukas and A Wibowo},
  title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach},
  journal = {Journal of Physics: Conference Series},
  abstract = {Nowadays mixing one language with another language either in
  spoken or written communication has become a common practice for bilingual
  speakers in daily conversation as well as in social media. Lexicon based
  approach is one of the approaches in extracting the sentiment analysis. This
  study is aimed to compare two lexicon models which are SentiNetWord and VADER
  in extracting the polarity of the code-mixed sentences in Indonesian language
  and Javanese language. 3,963 tweets were gathered from two accounts that
  provide code-mixed tweets. Pre-processing such as removing duplicates,
  translating to English, filter special characters, transform lower case and
  filter stop words were conducted on the tweets. Positive and negative word
  score from lexicon model was then calculated using simple mathematic formula
  in order to classify the polarity. By comparing with the manual labelling,
  the result showed that SentiNetWord perform better than VADER in negative
  sentiments. However, both of the lexicon model did not perform well in
  neutral and positive sentiments. On overall performance, VADER showed better
  performance than SentiNetWord. This study showed that the reason for the
  misclassified was that most of Indonesian language and Javanese language
  consist of words that were considered as positive in both Lexicon model.}
}
```

## License

cc_by_3.0

## Homepage

[https://iopscience.iop.org/article/10.1088/1742-6596/1869/1/012084](https://iopscience.iop.org/article/10.1088/1742-6596/1869/1/012084)

### NusaCatalogue

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