Narcissistic traits are increasingly expressed through online behavior, yet traditional assessment methods often struggle to capture these traits in naturalistic digital contexts. To address this gap, a novel computational framework ATTUNE is introduced for detecting narcissistic personality traits from Twitter data. The framework integrates attention-based language models, zero-shot learning, and psycholinguistic feature extraction (e.g., via Empath) to classify tweets into different categories of narcissism. A hybrid labeling strategy was employed, combining rule-based heuristics, fuzzy matching, and multi-model consensus to enhance scalability and contextual sensitivity. This approach employed data preprocessing, hybrid annotation, and feature-enriched classification, and was able to achieve a Label Completeness score of 64.84% and Model Consistency of 86.84%. These results surpassed earlier LIWC-based approaches, which reported 59% completeness. By reducing reliance on self-report measures and leveraging naturalistic language use, this work offers a scalable and context-aware method for computational assessment of personality traits in digital environments