Enhancing emotion detection on Twitter: an ensemble clustering approach utilizing emojis and keywords across multilingual datasets

Social media has become a vital element of everyday life, shaping domains like business, politics, and personal interactions. Emotions play a critical role in these areas, necessitating accurate detection and interpretation, especially on platforms like Twitter (X), which feature short texts, various data formats (such as words, Emojis, and numbers), and multilingual content, including dialects. This study explores the importance of Emojis and keywords in positively interpreting emotions on Twitter (X). It uses ensemble-clustering techniques, combining different clustering algorithms like KMeans with various methods for a detailed analysis of emotional subtleties in social media discourse. By merging the semantic meanings of Emojis and keywords, a novel clustering ensemble algorithm is proposed to improve emotion detection accuracy. The approach is tested on two datasets: English and Arabic dataset, using the Ekman model, which classifies emotions into six basic categories (joy, sadness, anger, disgust, surprise, and fear). The findings from this integrated method show greater accuracy and precision compared to individual methods, providing valuable insights into public sentiments, enhancing customer satisfaction analysis, and improving social media monitoring tools.