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README.md

@@ -5,4 +5,56 @@ tags:
 language:
 - jav
 - ind
----
+---
+
+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
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)