Unraveling Negation in Modern Greek Using Machine Learning: A Comprehensive Analysis and Detection Framework

Negation detection, a crucial component of natural language processing (NLP), plays a vital role in deciphering sentiment, context, and meaning in textual data. This study delves into the complexity of negation detection in the Greek language, providing a thorough examination of linguistic challenges that set Greek apart from other languages. Greek presents distinctive challenges for negation detection owing to its intricate morphology, complex syntax, and nuanced expressions of negation. In contrast to languages with rigid word order, Greek relies on inflections and particles, demanding NLP models to adeptly navigate diverse linguistic structures. This research reviews prevailing methodologies and tools for negation detection in Greek, encompassing both rule-based and machine learning-based approaches. Through the utilization of linguistic resources and annotated corpora specific to the Greek language, including a X (Twitter) corpus, we assess the performance of these methods, deliberating on their effectiveness in capturing the subtle nuances of negation. It is the first time that negation is being detected in Greek tweets and the annotation process was performed manually. In conclusion, this study offers valuable insights into the inherent challenges of negation detection in the Greek language. It provides a roadmap for researchers and practitioners in the NLP field to craft more precise and context-aware models tailored to the unique linguistic characteristics of Greek.