Social media platforms generate vast streams of emotionally rich textual data, offering valuable opportunities for critical applications, including mental health assessment and the analysis of collective public sentiment. However, detecting emotions in noisy and multilingual content remains challenging, particularly for under-resourced varieties such as dialects. Moreover, supervised learning techniques strongly depend on the availability of manually annotated corpora, whose creation requires substantial human effort and domain expertise. In contrast, unsupervised methods, while avoiding the need for human intervention, often lack sufficient robustness when confronted with the variability and complexity of natural language across diverse linguistic and cultural contexts. We present an ensemble clustering framework that automatically generates emotion labels from Twitter data, without human intervention. Our approach incorporates three emoji-handling strategies in the preprocessing step, enabling diverse semantic representations of emojis. We applied the BERT embeddings combined with PCA for dimensionality reduction within the same experimental framework. An ensemble clustering strategy integrating K-Means, Agglomerative clustering, and Gaussian Mixture Models (GMM) is adopted using multiple ensemble configurations. Experimental evaluation conducted on 10017 English tweets and 4134 Arabic tweets demonstrates that the proposed method achieves a silhouette score of 0.808 on English data using K-Means with Agglomerative and K-Means with GMM ensemble configurations. For Arabic data, silhouette scores of 0.728 and 0.718 are obtained using English and Arabic keywords, respectively. Emoji semantic integration enhances ensemble clustering performance, suggesting its importance for contextual disambiguation. The proposed framework provides a scalable solution for emotion detection in low-resource languages, enabling language-aware applications in multilingual contexts, particularly within linguistically diverse and multilingual populations