# Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation

arXiv: https://arxiv.org/abs/2009.09309 · HTML: https://mlsanity.com/paper/2009.09309/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://mlsanity.com/paper/2310.04928/) (2023)
- [SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://mlsanity.com/paper/2406.10118/) (2024)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models](https://mlsanity.com/paper/2409.17892/) (2024)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon](https://mlsanity.com/paper/2402.02113/) (2024)
- [NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages](https://mlsanity.com/paper/2309.10661/) (2023)
- [Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages](https://mlsanity.com/paper/2404.01009/) (2024)
- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages](https://mlsanity.com/paper/2206.07238/) (2022)
- [Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia](https://mlsanity.com/paper/2304.02746/) (2023)
- [Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia](https://mlsanity.com/paper/2311.00998/) (2023)

## Abstract (arXiv)

Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly attempted to define the morphology and syntax of Minangkabau, information processing in this language is still absent due to the scarcity of the annotated resource. In this work, we release two Minangkabau corpora: sentiment analysis and machine translation that are harvested and constructed from Twitter and Wikipedia. We conduct the first computational linguistics in Minangkabau language employing classic machine learning and sequence-to-sequence models such as LSTM and Transformer. Our first experiments show that the classification performance over Minangkabau text significantly drops when tested with the model trained in Indonesian. Whereas, in the machine translation experiment, a simple word-to-word translation using a bilingual dictionary outperforms LSTM and Transformer model in terms of BLEU score.
