Sentiment Analysis of the Palestine-Israel Crisis on Social Media using Convolutional Neural Network - Delva, Dwina Sarah; Lhaksmana, Kemas Muslim (Forum Kerjasama Pendidikan Tinggi, 2024)
The issue of Palestine and Israel is currently ongoing and is becoming increasingly heated. The struggle for territory and power is the reason for this conflict, thus attracting the world’s attention, especially that of the the Indonesians. People actively express various views in the form of opinions via social media platforms such as Twitter. Communities are competing to make posts and tweet as a form of support for either party. Various tweets appear, making it difficult to draw conclusions through manual analysis. Therefore, this study employs automatic sentiment analysis to enable mass data processing. The sentiment analysis process uses a Deep Learning algorithm, specifically the Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) is a Neural Network algorithms designed for visual shape processing and developed for classification tasks. Based on the explanation provided, it is expected to provide high accuracy and achieve the designed goals. This sentiment analysis research needs to be conducted because to understand and classify various forms of public sentiment toward the issue of Palestine and Israel, thereby providing an overview of the fluctuations in public sentiment concerning this matter in Indonesia. Outcomes of this investigation found the highest performance was achieved by the Convolutional Neural Network (Oversampling) algorithm with accuracy of 93.85%, precision of 93.76%, recall of 93.95%, and F1-score of 93.86%.