<![CDATA[Generation Alpha is a group growing up in an era of rapid digital technology advancement. Unlike previous generations who experienced a transition into technology, Generation Alpha demonstrates unique communication characteristics, particularly in their frequent use of slang, which is often difficult for older generations to understand. This gap in language understanding can lead to miscommunication, especially when the meaning of slang is misinterpreted. This phenomenon presents a challenge in establishing intergenerational communication, especially in digital and social media contexts where informal language is dominant. This study aims to explore the effectiveness of AI models in analyzing the sentiment of slang language used by Generation Alpha. Three BERT-based models were utilized in this research: BERT, RoBERTa, and DistilBERT. These models were selected based on their performance and efficiency in natural language processing (NLP) tasks, particularly in text classification and sentiment analysis. The dataset consists of 24,958 slang-based posts collected from users on the social media platform X. The analysis shows that DistilBERT achieved the highest accuracy score of 0.83, followed by BERT (0.82) and RoBERTa (0.81). These findings suggest that BERT-based models, especially DistilBERT, perform reliably in identifying the sentiment behind slang expressions used by Generation Alpha and hold potential for implementation in AI-based moderation or social media monitoring systems.]]>
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