As code-switching on social media becomes more common, the amount of bilingual users and code-switched data is rapidly increasing. This presents unique challenges for sentiment analysis, which has traditionally focused on monolingual text. Sentiment analysis involves categorizing sentiments from comments, reviews, or tweets into positive, negative, or neutral. To address the scarcity of research in code-switched sentiment analysis, this paper contributes the first code-switched corpus for Egyptian Arabic-English sentiment analysis (EESA), featuring 4,100 annotated YouTube comments. Another contribution is the implementation of various sentiment analysis models, including traditional neural models (BiLSTM-Attention and Hybrid-Transformer) and the utilization of advanced language models (Gemini and GPT). Traditional models, using non-contextual, contextual, and character embeddings with additional word features, achieved an ensemble test F1-score of 92.54%. Advanced models, evaluated in zero-shot and fine-tuned configurations, showed significant potential. Gemini-1.5 and GPT-4o performed well as zero-shot models, and the fine-tuned GPT-3.5 model achieved the highest test F1-score of 92.67%. This work compares traditional and advanced models, highlighting advancements in code-switched sentiment analysis.