The rising prevalence of social media turns them into huge, rich repositories of human emotions. Understanding and categorizing human emotion from social media content is of fundamental importance for many reasons, such as improvement of user experience, monitoring of public sentiment, support for mental health, and enhancement of focused marketing strategies. However, social media text is often unstructured and ambiguous; hence, extracting meaningful emotional information is difficult. Thus, effective emotion classification needs advanced techniques. This article proposes a novel model, EmoBERTa-X, to enhance performance in multilabel emotion classification, particularly in informal and ambiguous social media texts. Attention mechanisms combined with ensemble learning, supported by preprocessing steps, help in avoiding issues such as class imbalance of the dataset, ambiguity in short texts, and the inherent complexities of multilabel classification. The experimental results on the GoEmotions dataset indicate that EmoBERTa-X has outperformed state-of-the-art models on fine-grained emotion-detection tasks in social media expressions with an accuracy increase of 4.32% over some popular approaches.