English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Aspect-based sentiment analysis is a method in natural language processingaimed at identifying and understanding sentiments related to specific aspectsof an entity. Aspects are words or phrases that represent an aspect orattribute of a particular entity. Previous research has utilized generativepre-trained language models to perform aspect-based sentiment analysis.LEGO-ABSA is one framework that has successfully employed generativepre-trained language models in aspect-based sentiment analysis, particularly inEnglish. LEGO-ABSA uses a multitask learning and prompting approach to enhancemodel performance. However, the application of this approach has not been donein the context of Bahasa Indonesia. Therefore, this research aims to implementthe multitask learning and prompting approach in aspect-based sentimentanalysis for Bahasa Indonesia using generative pre-trained language models. Inthis study, the Indo LEGO-ABSA model is developed, which is an aspect-basedsentiment analysis model utilizing generative pre-trained language models andtrained with multitask learning and prompting. Indo LEGO-ABSA is trained with ahotel domain dataset in the Indonesian language. The obtained results includean 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 PairExtraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion TermExtraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5model, specifically mT5, by applying multitask learning to train all taskswithin aspect-based sentiment analysis.

Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language