AcehXBERT-sentiment

AcehXBERT Sentiment Analysis

AcehXBERT Sentiment is a sequence classification model fine-tuned from AcehXBERT-base, a BERT based language model pretrained with the MLM objective on Acehnese text. The model performs three class sentiment classification (negative, neutral, positive) and is tailored for Acehnese language data. The model is developed to support Natural Language Processing (NLP) for low-resource regional languages, particularly Acehnese, which remains underrepresented in digital resources.

Model Architecture

• Base model : AcehXBERT-base

• Fine-tuning Task : Sequence Classification

• Number of Labels : 3

• Language : Acehnese (ace)

How to Use

from transformers import pipeline

model_name = "doniNlp/AcehXBERT-sentiment"

AcehXBERT_classifier = pipeline("text-classification",model=model_name)

label_map = {
    "LABEL_0": "Negative",
    "LABEL_1": "Neutral",
    "LABEL_2": "Positive"
}

result = AcehXBERT_classifier("Rugo jak sikula manyang menyo utak kah lambat")[0]
result["label"] = label_map[result["label"]]

result

Dataset Overview

AcehX Corpus

A large unlabeled Acehnese corpus collected from:

• literature, folklore, oral traditions
• traditional customs and customary law texts
• Wikipedia
• YouTube comments

Authors

• Doni Sumito Sukiswo (Universitas Syiah Kuala (USK))
• Hammam Riza* (National Research and Innovation Agency (BRIN))
• Muhammad Subianto (Universitas Syiah Kuala (USK))
• Taufik Fuadi Abidin (Universitas Syiah Kuala (USK))
• Afnan (Universitas Syiah Kuala (USK))

* Corresponding Author

Credits

AcehXBERT Base is developed by :

<div style="display: flex;"> <a href="https://github.com/donisumito"> <img src="https://github.com/donisumito.png" alt="GitHub Profile" style="border-radius: 50%;width: 70px;height: 70px;margin:0 2px;"> </a> <a href="https://scholar.google.com/citations?user=j6Q7xnAAAAAJ&hl=id"> <img src="https://scholar.googleusercontent.com/citations?view_op=medium_photo&user=j6Q7xnAAAAAJ&citpid=1" alt="Google Scholar" style="border-radius: 50%;width: 70px;height: 70px;margin:0 2px;"> </a> <a href="https://scholar.google.com/citations?user=eVPeQT8AAAAJ&hl=en"> <img src="https://scholar.googleusercontent.com/citations?view_op=medium_photo&user=eVPeQT8AAAAJ&citpid=25" alt="Google Scholar" style="border-radius: 50%;width: 70px;height: 70px;margin:0 2px;"> </a> <a href="https://scholar.google.com/citations?user=dQ9hP8kAAAAJ&hl=en"> <img src="https://scholar.googleusercontent.com/citations?view_op=medium_photo&user=dQ9hP8kAAAAJ&citpid=3" alt="Google Scholar" style="border-radius: 50%;width: 70px;height: 70px;margin:0 2px;"> </a> <a href="https://scholar.google.com/citations?user=wVSSurkAAAAJ&hl=en"> <img src="https://scholar.googleusercontent.com/citations?view_op=medium_photo&user=wVSSurkAAAAJ&citpid=1" alt="Google Scholar" style="border-radius: 50%;width: 70px;height: 70px;margin:0 2px;"> </a> </div> <p />

Citation

You can find the full details in our paper:
Development of AcehX for Sentiment Analysis Using a BERT-Based Model

Proceedings of the 28th O-COCOSDA Conference, IEEE, 2025

https://doi.org/10.1109/O-COCOSDA68185.2025.11384824

If you use AcehX or AcehXBERT, please cite:

@INPROCEEDINGS{11384824,
  author={Sukiswo, Doni Sumito and Riza, Hammam and Subianto, Muhammad and Abidin, Taufik Fuadi and Afnan},
  booktitle={2025 28th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)}, 
  title={Development of Acehx for Sentiment Analysis Using a Bert-Based Model}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Training;Adaptation models;Sentiment analysis;Analytical models;Vocabulary;Technological innovation;Linguistics;Transformers;Cultural differences;Artificial intelligence;AcehX;sentiment analysis;NLP;transformers;BERT;deep learning},
  doi={10.1109/O-COCOSDA68185.2025.11384824}}