International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Sentiment Analysis of Imbalanced Sarcastic Flood Disaster Texts Using Deep Learning Models

Nur KhamidahStudent, Department of Statistics and Data Science, IPB University, Bogor, IndonesiaKhairil Anwar NotodiputroLecturer, Department of Statistics and Data Science, IPB University, Bogor, IndonesiaSachnaz Desta OktarinaProfessor Lecturer, Department of Statistics and Data Science, IPB University, Bogor, Indonesia

Vol 8 No 11 (2024): Volume 8, Issue 11, November 2024 | Pages: 150-158

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 17-11-2024

doi Logo doi.org/10.47001/IRJIET/2024.811015

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Abstract

Sentiment analysis often faces challenges like manual labeling, sarcasm detection, and imbalanced class labels. Using Twitter/X data for sentiment analysis is resource-intensive due to manual labeling. The BERT model is adequate for Indonesian sentiment analysis, but sarcasm remains challenging. This research evaluates the performance of BERT, LSTM, and BERT-LSTM models for classifying sarcastic text data, specifically in flood-related posts from Indonesia. We used Twitter/X data from December 19, 2023, to January 13, 2024, labeled by three annotators. We handle imbalanced data using techniques like Random Undersampling, SMOTE, and SMOTETomek. We assessed model performance with ANOVA based on balance-weighted accuracy. The BERT and BERT-LSTM models excelled, achieving balance-weighted accuracy values of 98.61% and 98.06%, respectively. This research advances sentiment analysis methods, particularly for natural disaster contexts in Indonesia.

Keywords

sentiment, flood, sarcasm, imbalance data, deep learning model


Citation of this Article

            

Nur Khamidah, Khairil Anwar Notodiputro, & Sachnaz Desta Oktarina. (2024). Sentiment Analysis of Imbalanced Sarcastic Flood Disaster Texts Using Deep Learning Models. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(11), 150-158. Article DOI https://doi.org/10.47001/IRJIET/2024.811015

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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