holylovenia
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README.md
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language:
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- jav
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- ind
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---
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language:
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- jav
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- ind
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---
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Sentiment analysis and machine translation data for Javanese and Indonesian.
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## Dataset Usage
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Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
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## Citation
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@article{Tho_2021,
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doi = {10.1088/1742-6596/1869/1/012084},
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url = {https://doi.org/10.1088/1742-6596/1869/1/012084},
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year = 2021,
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month = {apr},
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publisher = {{IOP} Publishing},
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volume = {1869},
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number = {1},
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pages = {012084},
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author = {C Tho and Y Heryadi and L Lukas and A Wibowo},
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title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach},
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journal = {Journal of Physics: Conference Series},
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abstract = {Nowadays mixing one language with another language either in
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spoken or written communication has become a common practice for bilingual
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speakers in daily conversation as well as in social media. Lexicon based
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approach is one of the approaches in extracting the sentiment analysis. This
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study is aimed to compare two lexicon models which are SentiNetWord and VADER
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in extracting the polarity of the code-mixed sentences in Indonesian language
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and Javanese language. 3,963 tweets were gathered from two accounts that
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provide code-mixed tweets. Pre-processing such as removing duplicates,
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translating to English, filter special characters, transform lower case and
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filter stop words were conducted on the tweets. Positive and negative word
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score from lexicon model was then calculated using simple mathematic formula
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in order to classify the polarity. By comparing with the manual labelling,
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the result showed that SentiNetWord perform better than VADER in negative
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sentiments. However, both of the lexicon model did not perform well in
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neutral and positive sentiments. On overall performance, VADER showed better
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performance than SentiNetWord. This study showed that the reason for the
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misclassified was that most of Indonesian language and Javanese language
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consist of words that were considered as positive in both Lexicon model.}
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}
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## License
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cc_by_3.0
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## Homepage
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### NusaCatalogue
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For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
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