Abstract
This study explores the application of sentiment analysis in news headlines as a means of enhancing language learning, with a focus on how sentiment polarity influences vocabulary retention, learner engagement, and comprehension. It seeks to develop a practical framework for integrating sentiment-analyzed headlines into second language (L2) activities. Using a mixed-methods approach, the research combines quantitative content analysis and computational sentiment analysis to examine sentiment patterns in Libya-related news headlines from Al-Jazeera and BBC (2024–2025). The findings reveal distinct editorial tendencies between the two outlets: Al-Jazeera’s coverage demonstrates a relatively balanced sentiment distribution, whereas BBC’s headlinesexhibit a significantly more negative tone. These results highlight the potential of sentiment-filtered news content as a pedagogical tool as they offer insights into how emotional framing in media can shape language acquisition outcomes.