This research focuses on improving sentiment analysis for the Moroccan dialect (Darija) by transcoding it into Modern Standard Arabic (MSA) to leverage existing NLP tools. Darija presents unique challenges for natural language processing due to its lack of extensive computational resources and significant linguistic differences from MSA. Initially, we developed a supplementary dictionary for Darija words not found in standard Arabic resources. This approach allowed us to convert Darija text into MSA, facilitating the use of advanced MSA-based sentiment analysis tools. However, this method revealed limitations in accurately capturing the context of words, leading to potential misinterpretations, especially for words with multiple meanings. To address this, we integrated multilingual embeddings into the transcoding process, enabling better contextualization of words during their conversion from Darija to MSA. This approach ensures that words are transposed to their correct MSA equivalents based on their contextual meaning. We tested three classical machine learning models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB)—on the MAC Dataset, both before and after applying our enhanced transcoding method. Our results demonstrate a significant improvement in model accuracy, with the SVM model’s accuracy increasing from 62.4% to 86.1% after incorporating multilingual embeddings. This study not only optimizes sentiment analysis for Darija but also offers a scalable solution for other Arabic dialects, emphasizing the importance of context-aware transcoding in NLP.