<![CDATA[The geopolitical issue between Israel and Palestine has sparked a wide range of public reactions on social media, particularly on the platform X. Sentiment analysis is a widely discussed topic in Natural Language Processing. This study develops a sentiment classification model for that issue using the BERT Base Uncased architecture, with data labeled automatically through the VADER approach. Data were collected via scraping with Tweet-Harvest, yielding 3.172 tweets, later reduced to 2.362 after preprocessing stages including cleaning, case folding, tokenizing, stopword removal, and stemming. VADER labeling produced a distribution of 73.7% negative sentiment and 26.3% positive sentiment. The model achieved 86.92% accuracy, with precision, recall, and F1-Score for the negative class of 90%, 93%, and 91%, respectively, while the positive class obtained 77%, 71%, and 74%. The average F1-score reached 88%. These results indicate the model correctly predicts most of the data, although a slight indication of overfitting appeared, with no significant impact on validation accuracy. This confirms that combining VADER and BERT Base Uncased is effective for sentiment classification on international geopolitical issues.]]>
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