Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis
  for Indonesian Language
Randy Zakya Suchrady Ayu Purwarianti
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. Previous research has utilized generative
pre-trained language models to perform aspect-based sentiment analysis.
LEGO-ABSA is one framework that has successfully employed generative
pre-trained language models in aspect-based sentiment analysis, particularly in
English. 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 Bahasa Indonesia. Therefore, this research aims to implement
the multitask learning and prompting approach in aspect-based sentiment
analysis for Bahasa Indonesia using generative pre-trained language models. In
this study, the Indo LEGO-ABSA model is developed, which is an aspect-based
sentiment analysis model utilizing generative pre-trained language models 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 task, 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.