We investigate the use of Aspect-Based Sentiment Analysis (ABSA) to analyze polarization in online discourse. For the analysis, we use a corpus of over 3 million user comments and replies from four state-funded media channels from YouTube Shorts in the context of the 2023 Israel–Hamas war. We first annotate a subsample of approx. 5 000 comments for positive, negative, and neutral sentiment towards a list of topic related aspects. After training an ABSA model (Yang et al., 2023) on the corpus, we evaluate its performance on this task intrinsically, before evaluating the usability of the automatic analysis of the whole corpus for analyzing polarization. Our results show that the ABSA model achieves an F1 score of 77.9. The longitudinal and outlet analyses corroborate known trends and offer subject experts more fine-grained information about the use of domain-specific language in user-generated content.