Arezou Zahiri Pourzarandi

Arezou Zahiri Pourzarandi, Farshad Jafari

This study employs Natural Language Processing (NLP) to analyze the intricate linguistic and emotional dimensions within the plays of Bernard-Marie Koltès, a central figure in contemporary French theatre. By integrating advanced computational techniques, we dissect Koltès' narrative style, revealing the subtle interplay between language and emotion across his dramatic oeuvre. Our findings highlight how Koltès crafts his narratives, enriching our understanding of his thematic explorations and contributing to the broader field of digital humanities in literary analysis.

Arezou Zahiri Pourzarandi, Farshad Jafari

Exploring the depths of Samuel Beckett's "Not I" through advanced natural language processing techniques, this research uncovers the intricate linguistic structures that underpin the text. By analyzing word frequency, detecting emotional sentiments with a BERT-based model, and examining repetitive motifs, we unveil how Beckett's minimalist yet complex language reflects the protagonist's fragmented psyche. Our results demonstrate that recurring themes of time, memory, and existential angst are artfully woven through recursive linguistic patterns and rhythmic repetition. This innovative approach not only deepens our understanding of Beckett's stylistic contributions but also highlights his unique role in modern literature, where language transcends simple communication to explore profound existential questions.