POS Tagging for Informal Data in Platform X Using Word2Vec and Bidirectional LSTM

Unstructured and informal Indonesian text poses challenges for NLP tasks like POS tagging. This study applies a Bidirectional LSTM with Skip-Gram word embeddings to capture contextual and semantic meaning, achieving a 94.52% F1 score. The approach shows strong potential for improving linguistic analysis and broader NLP applications in Indonesian.