SEACrowd/code_mixed_jv_id

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at dc88250
Licence files: LICENSE, licence not identifiedEvidence: LICENSE at dc88250
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@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

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dataset_licence({ "id": "SEACrowd/code_mixed_jv_id" })
signal: "unclear"
stated_licence: null
sources: 0