This study examines the impact of information and emotional content on user engagement in social media, focusing on ten Japanese cosmetic brands on X (formerly Twitter). To classify content types, we conduct an annotation task with three human evaluators to label 1,000 contents, which then served to train a Support Vector Machine (SVM) classifier. Using text analytics, the emotional content ratio of each content is quantified and its impact on user engagement is analyzed. The correlation analysis revealed a positive association between emotional content and engagement for most brands, with one exception. Furthermore, we divided the emotional content ratio into ten equally sized groups from 0 to 1. A one-way ANOVA demonstrated that the effect of different emotional content ratio on user engagement is statistically significant. Moreover, content with an emotional content ratio between 0.6 and 0.7, paired with an information content ratio of 0.3 to 0.4, achieves the highest engagement compared to contents with lower or higher emotional ratios.