Deep Learning Approach for Aspect-Based Sentiment Analysis on Indonesian Hospitals Reviews

Fahmi, Muhamad and Yudanto, Faturahman and Nazhifah, Naurah and Sari, Yunita and Afiahayati, Afiahayati (2023) Deep Learning Approach for Aspect-Based Sentiment Analysis on Indonesian Hospitals Reviews. In: 2023 8th International Conference on Informatics and Computing, ICIC 2023, 8 - 9 December 2023, Hybrid, Malang.

[thumbnail of 1889.Deep_Learning_Approach_for_Aspect-Based_Sentiment_Analysis_on_Indonesian_Hospitals_Reviews.pdf] Text
1889.Deep_Learning_Approach_for_Aspect-Based_Sentiment_Analysis_on_Indonesian_Hospitals_Reviews.pdf - Published Version
Restricted to Registered users only

Download (555kB) | Request a copy

Abstract

Hospitals are individual health service facilities that provide inpatient and outpatient care, therefore, quality hospital services, facilities, and resources are a necessity and must be met by every hospital. To assess the quality of hospital services, facilities, and resources, we can see reviews from users through reviews available on Google Maps. Using natural language understanding, we can identify the sentiment associated with each particular aspect. In this research, we developed a deep learning model which can classify three aspects (services, human resources, and facilities) with four sentiments (positive, negative, neutral, and none) using a multiclass-multioutput deep learning model, ensuring both aspect and sentiment classification within a single model. Our research concludes that the BERT-LSTM model demonstrates promising performance with an F1-score of 0.597, but faces challenges in accurately classifying neutral sentiment across all
aspects due to the lack of neutral sentiment labels

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: aspect based sentiment analysis; BERT; BiLSTM; deep learning; hospital; LSTM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Masrumi Fathurrohmah
Date Deposited: 22 Aug 2024 07:08
Last Modified: 22 Aug 2024 07:08
URI: https://ir.lib.ugm.ac.id/id/eprint/2862

Actions (login required)

View Item
View Item