In this study, corpus-assisted sentiment analysis is used to explore how news headlines frame the Palestine–Israel conflict by analyzing a dataset consisting of 5000 news headlines retrieved from Google News. The sentiment of headlines was classified as positive, negative, or neutral, using a supervised machine learning approach of logistic regression. The sentiment analysis results reveal a dominance of neutral sentiment, with considerable positive sentiment, which is indicative of the resolution-centered framing employed by the media outlets. The study adopts the Media Framing theory and Social Identity theory to establish how media framing impacts public perception and reinforces social identity-based bias. The results highlight the importance of better balanced and solution-oriented reporting in support of conflict resolution efforts. However, the strong presence of negative sentiment, on the contrary, is indicative of media outlets' conflict-oriented framing.  Sentiment analysis could be used as a useful tool for policymakers, journalists, and researchers to gauge the effect of media storytelling on public opinion. For future research, multilingual and longitudinal sentiment analysis can be extended to analyze the changing media discourses in a different cultural context.