NLP Bahasa Indonesia Resources

This repository provides link to useful dataset and another resources for NLP in Bahasa Indonesia.

Last Update: 24 Des 2020

Dictionary

Sentiment Words

1) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negatifta2.txt 2) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negativeadd.txt 3) (Negative) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/negativekeyword.txt 4) (Negative) https://github.com/masdevid/ID-OpinionWords/blob/master/negative.txt 5) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positifta2.txt 6) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positiveadd.txt 7) (Positive) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/positivekeyword.txt 8) (Positive) https://github.com/masdevid/ID-OpinionWords/blob/master/positive.txt 9) (Score) https://github.com/agusmakmun/SentiStrengthID/blob/master/iddict/sentimentword.txt 10) (InSet Lexicon) https://github.com/fajri91/InSet [Paper] 11) (Twitter Labelled Sentiment) https://www.researchgate.net/profile/RidiFerdiana/publication/339936724IndonesianSentimentTwitterDataset/data/5e6d64c6a6fdccf994ca18aa/Indonesian-Sentiment-Twitter-Dataset.zip?origin=publicationDetail_linkedData [Paper]

Position / Degree Words

1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/psuf.txt 2) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lldr.txt 3) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/opos.txt 4) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ptit.txt

Root Words

1) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/rootword.txt 2) https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.original.txt 3) https://github.com/sastrawi/sastrawi/blob/master/data/kata-dasar.txt 4) https://github.com/prasastoadi/serangkai/blob/master/serangkai/kamus/data/kamus-kata-dasar.csv

I have made the combined root words list from all of the above repositories.

Slang Words

1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/kbba.txt 2) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/slangword.txt 3) https://github.com/panggi/pujangga/blob/master/resource/formalization/formalizationDict.txt

I have made the combined slang words dictionary from all of the above repositories.

Stop Words

1) https://github.com/yasirutomo/python-sentianalysis-id/blob/master/data/feature_list/stopwordsID.txt 2) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/stopword.txt 3) https://github.com/abhimantramb/elang/tree/master/word2vec/utils/stopwords-list

I have made the combined stop words list from all of the above repositories.

Emoticon

1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/emoticon.txt 2) https://github.com/jolicode/emoji-search/blob/master/synonyms/cldr-emoji-annotation-synonyms-id.txt 3) https://github.com/agusmakmun/SentiStrengthID/blob/master/id_dict/emoticon.txt

Acronym

1) https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/acronym.txt 2) https://github.com/panggi/pujangga/blob/master/resource/sentencedetector/acronym.txt 3) https://id.wiktionary.org/wiki/Lampiran:DaftarsingkatandanakronimdalambahasaIndonesia#A

Indonesia Region

1) https://github.com/abhimantramb/elang/blob/master/word2vec/utils/indonesian-region.txt 2) https://github.com/edwardsamuel/Wilayah-Administratif-Indonesia/tree/master/csv 3) https://github.com/pentagonal/Indonesia-Postal-Code/tree/master/Csv

Swear Words

1) https://github.com/abhimantramb/elang/blob/master/word2vec/utils/swear-words.txt

Composite Words

1) https://github.com/panggi/pujangga/blob/master/resource/tokenizer/compositewords.txt

Country

1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/country.txt

Region Words

1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/lpre.txt

Title of Name Words

1) https://github.com/panggi/pujangga/blob/master/resource/netagger/contextualfeature/ppre.txt

Gender by Name

1) https://github.com/seuriously/genderprediction/blob/master/namatraining.txt

Organization Words

1) https://github.com/panggi/pujangga/blob/master/resource/reference/opre.txt

Number Words

1) https://github.com/panggi/pujangga/blob/master/resource/netagger/morphologicalfeature/number.txt

Calendar Words

1) https://github.com/onlyphantom/elang/blob/master/build/lib/elang/word2vec/utils/negative/calendar-words.txt

Named Entity Recognition Dataset

1) Product NER. https://github.com/dziem/proner-labeled-text 2) NER-grit. https://github.com/grit-id/nergrit-corpus

POS-Tagging Dataset

1) https://github.com/famrashel/idn-tagged-corpus 2) https://github.com/kmkurn/id-pos-tagging/blob/master/data/dataset.tar.gz

Question and Answering Dataset

1) https://github.com/google-research-datasets/tydiqa

Hate-speech Dataset

1) https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection

Text Summarization Dataset

1) https://github.com/kata-ai/indosum

Analogy Word Dataset

1) https://github.com/kata-ai/kawat

Unsupervised Corpus

1) OSCAR. https://oscar-corpus.com/ 2) Online Newspaper. https://github.com/feryandi/Dataset-Artikel

Puisi & Pantun dataset

1) https://github.com/ilhamfp/puisi-pantun-generator

POS-Tagging

1) https://medium.com/@puspitakaban/pos-tagging-bahasa-indonesia-dengan-flair-nlp-c12e45542860 2) Manually Tagged Indonesian Corpus [Paper] [GitHub]

Pre-trained NLU Model

1) Indo-BERT. https://github.com/indobenchmark/indonlu & https://huggingface.co/indobenchmark/indobert-base-p1 1) Transformer-based Pre-trained Model in Bahasa. https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers 1) Generate Word-Embedding / Sentence-Embedding using pre-Trained Multilingual Bert model. (https://colab.research.google.com/drive/1yFphU6PW9Uo6lmDly_ud9a6c4RCYlwdX#scrollTo=Zn0n2S-FWZih). P.S: Just change the model using 'bert-base-multilingual-uncased' 2) https://github.com/meisaputri21/Indonesian-Twitter-Emotion-Dataset. [Paper] 3) https://github.com/Kyubyong/wordvectors 4) https://drive.google.com/uc?id=0B5YTktu2dOKKNUY1OWJORlZTcUU&export=download 5) https://github.com/deryrahman/word2vec-bahasa-indonesia 6) https://sites.google.com/site/rmyeid/projects/polyglot

Train Word Embedding by Your Self

1) (FastText). https://structilmy.com/2019/08/membuat-model-word-embedding-fasttext-bahasa-indonesia/ 2) (Word2Vec). https://yudiwbs.wordpress.com/2018/03/31/word2vec-wikipedia-bahasa-indonesia-dengan-python-gensim/

Usable Library

1) Pujangga: Indonesian Natural Language Processing REST API. https://github.com/panggi/pujangga 2) Sastrawi Stemmer Bahasa Indonesia. https://github.com/sastrawi/sastrawi 3) NLP-ID. https://github.com/kumparan/nlp-id 4) MorphInd: Indonesian Morphological Analyzer. http://septinalarasati.com/morphind/ 5) INDRA: Indonesian Resource Grammar. https://github.com/davidmoeljadi/INDRA 6) Typo Checker. https://github.com/mamat-rahmat/checker_id 7) Multilingual NLP Package. https://github.com/flairNLP/flair 9) spaCy [GitHub] [Tutorial] 9) https://github.com/yohanesgultom/nlp-experiments 10) https://github.com/yasirutomo/python-sentianalysis-id 11) https://github.com/riochr17/Analisis-Sentimen-ID 12) https://github.com/yusufsyaifudin/indonesia-ner

Translation

Sometimes there is an english word within our text and we have to translate it. We can exploit the english word dictionary provided here and we can use the Google Translate API for Python

Spelling Correction

You can adjust this code with Bahasa corpus to do the spelling correction

Topic Analysis

1) (Introduction to LSA & LDA). https://monkeylearn.com/blog/introduction-to-topic-modeling/ 2) (Introduction to LDA w/ Code & Tips). https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/ 3) (Topic Modeling Methods Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper21-ASurveyofTopicModelinginTextMining.pdf 4) (Original LDA Paper). http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf 5) (LDA Python Library). https://pypi.org/project/lda/; https://radimrehurek.com/gensim/models/ldamodel.html; https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html 6) (Original CTM Paper). http://people.ee.duke.edu/~lcarin/Blei2005CTM.pdf 7) (CTM Python Library). https://pypi.org/project/tomotopy/; https://github.com/kzhai/PyCTM 8) (Gaussian LDA Paper). https://www.aclweb.org/anthology/P15-1077.pdf 9) (Gaussian LDA Library). https://github.com/rajarshd/GaussianLDA 10) (Temporal Topic Modeling Comparison Paper). https://thesai.org/Downloads/Volume6No1/Paper21-ASurveyofTopicModelinginTextMining.pdf 11) (TOT: A Non-Markov Continuous-Time Model of Topical Trends Paper). https://people.cs.umass.edu/~mccallum/papers/tot-kdd06s.pdf 12) (TOT Library). https://github.com/ahmaurya/topicsover_time
13) (Example of LDA in Bahasa Project Code). https://github.com/kirralabs/text-clustering

Text Classification

Zero-shot Learning

1) (Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach) https://arxiv.org/pdf/1909.00161.pdf | https://github.com/yinwenpeng/BenchmarkingZeroShot 2) (Integrating Semantic Knowledge to Tackle Zero-shot Text Classification) https://arxiv.org/abs/1903.12626 | https://github.com/JingqingZ/KG4ZeroShotText 3) (Train Once, Test Anywhere: Zero-Shot Learning for Text Classification) https://arxiv.org/abs/1712.05972 | https://amitness.com/2020/05/zero-shot-text-classification/ 4) (Zero-shot Text Classification With Generative Language Models) https://arxiv.org/abs/1912.10165 | https://amitness.com/2020/06/zero-shot-classification-via-generation/ 5) (Zero-shot User Intent Detection via Capsule Neural Networks) https://arxiv.org/abs/1809.00385 | https://github.com/congyingxia/ZeroShotCapsule

Few-shot Learning

1) (Few-shot Text Classification with Distributional Signatures) https://arxiv.org/pdf/1908.06039.pdf | https://github.com/YujiaBao/Distributional-Signatures 2) (Few Shot Text Classification with a Human in the Loop) https://katbailey.github.io/talks/Few-shot%20text%20classification.pdf | https://github.com/katbailey/few-shot-text-classification 3) (Induction Networks for Few-Shot Text Classification) https://arxiv.org/pdf/1902.10482v2.pdf | https://github.com/zhongyuchen/few-shot-learning

Twitter Scraping:

1) GetOldTweets3. https://github.com/Mottl/GetOldTweets3

Usage:

bash
import GetOldTweets3 as got
tweetCriteria=got.manager.TweetCriteria().setQuerySearch('#CoronaVirusIndonesia').setSince("2020-01-01").setUntil("2020-03-05").setNear("Jakarta, Indonesia").setLang("id")
tweets=got.manager.TweetManager.getTweets(tweetCriteria)
for tweet in tweets:
    print(tweet.username)
    print(tweet.text)
    print(tweet.date)
    print("tweet.to")
    print("tweet.retweets")
    print("tweet.favorites")
    print("tweet.mentions")
    print("tweet.hashtags")
    print("tweet.geo")

2) Tweepy. http://docs.tweepy.org/en/latest/

Step-by-step how to use Tweepy. https://towardsdatascience.com/how-to-scrape-tweets-from-twitter-59287e20f0f1

Sign in to Twitter Developer. https://developer.twitter.com/en

Full List of Tweets Object. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object

Increasing Tweepy’s standard API search limit. https://bhaskarvk.github.io/2015/01/how-to-use-twitters-search-rest-api-most-effectively./

Other Resources:

1) https://github.com/irfnrdh/Awesome-Indonesia-NLP 2) https://github.com/kirralabs/indonesian-NLP-resources