Social media platforms have become increasingly essential to political communication, especially during high-salience election cycles when candidates rely on digital messaging to reach and mobilize voters. While both the sentiment and readability of written social media posts determine user engagement with those posts, they are often studied independently, leaving a limited understanding of how sentiment and readability interact together in real-world political discourse, where both emotional tone and linguistic accessibility vary. This research builds on that gap by examining tweets posted by U.S. presidential candidates Donald Trump and Joe Biden during the 2020 election, focusing on how readability and sentiment independently predict public engagement and how their interaction influences the individual effects. In this study, readability–the ease of comprehension of the text–is quantified using measures of sentence structure, lexical complexity, and processing fluency. Sentiment, the emotional polarity expressed in the text, is assessed through computational indicators of word valence, polarity, and affective intensity. Engagement with the social media posts is operationalized as observable user interactions, including likes and retweets. By applying established readability indices and sentiment analysis tools to campaign tweets, this research evaluates how these linguistic features relate to engagement outcomes through regression-based statistical modeling with interaction terms between readability and sentiment. By clarifying how linguistic accessibility and emotional tone jointly affect engagement in high-stakes political communication, I aim to provide insight into how political messages gain visibility online, informing broader discussions of accessibility, media literacy, and the role of language in digital democratic participation.