Implementation of IndoNLU Pre-Trained Model for Aspect-Based Sentiment Analysis of Indonesian Stock News

Abstract

Investors in Indonesia are increasing from year to year, especially mutual fund investors managed by investment managers. News is one of the factors considered by investment managers in making stock investment decisions. Very diverse news sources and different writing styles can make it difficult to retrieve information on each issuer in the news. In this research, the aspect-based sentiment analysis (ABSA) method is implemented to extract news specifically on each aspect (issuer) in the news and evaluate the issuer. The model used is a pre-trained Indonesian Bidirectional Encoder Representations from Transformers (BERT) model, IndoNLU, because the object of research is Indonesian-language stock news. The results show that with a combination of hyperparameters consisting of batch size 8, learning rate 0.00002, and epoch 8 for the IndoNLU model can produce an average evaluation metric value consisting of precision, recall, f1-score, and accuracy of 90%.