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README.md

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+---
+tags:
+- sentiment-analysis
+language:
+- ind
+---
+
+# indolem_sentiment
+
+IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
+
+
+
+This dataset is based on binary classification (positive and negative), with distribution:
+
+* Train: 3638 sentences
+
+* Development: 399 sentences
+
+* Test: 1011 sentences
+
+
+
+The data is sourced from 1) Twitter [(Koto and Rahmaningtyas, 2017)](https://www.researchgate.net/publication/321757985_InSet_Lexicon_Evaluation_of_a_Word_List_for_Indonesian_Sentiment_Analysis_in_Microblogs)
+
+and 2) [hotel reviews](https://github.com/annisanurulazhar/absa-playground/).
+
+
+
+The experiment is based on 5-fold cross validation.
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## Citation
+
+```
+@article{DBLP:journals/corr/abs-2011-00677,
+  author    = {Fajri Koto and
+               Afshin Rahimi and
+               Jey Han Lau and
+               Timothy Baldwin},
+  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
+               Model for Indonesian {NLP}},
+  journal   = {CoRR},
+  volume    = {abs/2011.00677},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2011.00677},
+  eprinttype = {arXiv},
+  eprint    = {2011.00677},
+  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+## License
+
+Creative Commons Attribution Share-Alike 4.0 International
+
+## Homepage
+
+[https://indolem.github.io/](https://indolem.github.io/)
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)