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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics
Marchel Yusuf Rumlawang Arpipi
Universitas Tarumanagara
Computer Science & IT

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Climate Change Sentiment Analysis using LSTM Marchel Yusuf Rumlawang Arpipi; Teny Handhayani; Janson Hendryli
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1719

Abstract

This research aims to observe the sentiment of Indonesian people towards climate change using the Long Short-Term Memory (LSTM) methods. The data samples used in this study are primary data that have been collecting by using the Twitter Application Programming Interface (API) that provides by a platform known as RapidAPI. This data sample is text data with 2425 total samples obtained during the time period from 01 January 2020 to 25 August 2024. The sentiment is classified as positive, negative, and neutral. The performance of the LSTM model is evaluate using accuracy, precision, recall, F1-score, and confusion matrix and then compare with other models such as Ensemble Model, Naive Bayes, and Linear SVC. By conducting Exploratory Data Analysis (EDA), it is reveals that the distribution of public sentiment towards climate change in Indonesia from the collected data is mostly positive. However, there are not many individuals that are still ignorant and skeptical about the issue, resulting in a negative sentiment that can be fatal to the environment and its surroundings. When comparing the Ensemble Model, Naive Bayes, and Linear SVC, the LSTM model successfully identifies the perception patterns between sentences according to their sentiments. LSTM obtains an accuracy of 60% and outperforms Ensemble Model, Naive Bayes, and Linear SVC. This research also highlights the technical challenges in processing and analyzing dynamic and diverse data so that the results obtained are better, especially in terms of data quality before further processing.
Co-Authors Janson Hendryli Teny Handhayani