Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language
Randy Zakya Suchrady, Ayu Purwarianti · 2023
Aspect-based sentiment analysis is a method in natural language processing aimed at identifying and understanding sentiments related to specific aspects of an entity. Aspects are words or phrases that represent an aspect or attribute of a particular entity. Earlier studies have applied generative pretrained language model for aspect-based sentiment analysis. An example of this is the LEGO-ABSA framework, which effectively utilized these models, specifically in English-based aspect-based sentiment analysis. LEGO-ABSA uses a multitask learning and prompting approach to enhance model performance. However, the application of this approach has not been done in the context of Indonesian language. Therefore, this research aims to implement the multitask learning and prompting approach in aspect-based sentiment analysis for Indonesian language using generative pretrained language model. In this study, the Indo LEGO-ABSA model is developed, which is an aspect-based sen-timent analysis model utilizing generative pretrained language model and trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a hotel domain dataset in the Indonesian language. The obtained results include an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction, 86.09% for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5 model, specifically mT5, by applying multitask learning to train all tasks within aspect-based sentiment analysis.11All works can be visited in https://github.com/rdyzakya/IndoLEGO-ABSA