Indonesian Text Files Dataset

Project Overview

Objective

Our mission was to create an extensive and diverse Indonesian text dataset to empower sophisticated natural language processing (NLP) models. This project aimed to advance the capabilities in areas such as language translation, sentiment analysis, and automated customer support systems.

Scope

We undertook a comprehensive project to compile and annotate a substantial Indonesian text dataset. This dataset is crucial for training NLP models to understand and process Indonesian language accurately, catering to a variety of applications in technology and communication sectors.

Sources

Local Partnerships: Forged relationships with local Indonesian businesses and educational institutions, resulting in the collection of over 120,000 unique text files.
Online Resources: Leveraged online Indonesian text resources, adding an additional 30,000 files to our dataset.
Public Contributions: Incorporated 20,000 text files from public platforms and open-source contributors.

Data Collection Metrics

Total Text Files: 170,000
Local Partnerships: 120,000
Online Resources: 30,000
Public Contributions: 20,000

Annotation Process

Stages

Content Categorization: Classified texts into various genres, such as news, literature, and academic, to facilitate targeted model training.
Linguistic Features: Annotated linguistic elements like syntax, semantics, and idiomatic expressions typical to the Indonesian language.
Sentiment Analysis: Tagged texts with sentiment labels (positive, negative, neutral) to enhance model’s emotional intelligence.

Annotation Metrics

Text Files Annotated: 170,000
Linguistic Features Tagged: 170,000
Sentiment Analysis Labels Assigned: 170,000

Quality Assurance

Continuous Data Review: Regular checks to ensure the richness and diversity of the dataset.
Privacy and Ethical Standards: Adherence to strict privacy guidelines, ensuring that all text data is ethically sourced and anonymized.
Feedback Integration: Collaboration with Indonesian language experts for continuous improvement of the dataset quality.

QA Metrics:

Data Diversity Score: 95%
Annotation Accuracy: 99.2%
User Feedback Acceptance: 90%

Conclusion

The development and deployment of our Indonesian text dataset have significantly impacted the field of NLP. This dataset not only enriches the linguistic resources available for Indonesian but also paves the way for more nuanced and accurate language models, contributing to the global digital landscape.