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Corresponding Author ORCID Identifer

Aripinhttps://orcid.org/0000-0001-8434-5775

Section

(Thematic) Augmented Intelligence

Abstract

The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on performance metrics to evaluate the ability of the classification model to distinguish between classes; 5) visualization of experimental results. The LSTM model is a modification of the recurrent neural network (RNN). The LSTM model has many advantages, including being able to remember a collection of information that has been stored for a long period of time, being able to delete information that is no longer relevant, and being more efficient in processing, predicting, and classifying data based on a certain time sequence. Another advantage is that LSTM's ability to identify temporal dependencies and contextual interactions in sequential data makes it suitable for social media text analysis. The model demonstrated success in sentiment classification on geopolitical topics with an impressive accuracy rate of 91%. The findings demonstrate deep learning's potential applications for sentiment analysis and offer insights into public opinion dynamics during times of international crises.

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