Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages
This work addresses the problem of data scarcity for NLP in Indonesian local languages, which is incremental as it applies existing methods to new data.
The paper tackled the challenge of constructing NLP datasets for low-resource Indonesian local languages by leveraging Twitter's geolocation tool for automatic annotation, resulting in a framework for creating, collecting, and classifying such datasets.
Twitter contains an abundance of linguistic data from the real world. We examine Twitter for user-generated content in low-resource languages such as local Indonesian. For NLP to work in Indonesian, it must consider local dialects, geographic context, and regional culture influence Indonesian languages. This paper identifies the problems we faced when constructing a Local Indonesian NLP dataset. Furthermore, we are developing a framework for creating, collecting, and classifying Local Indonesian datasets for NLP. Using twitter's geolocation tool for automatic annotating.