Exploring Linguistic Features in Sentiment Analysis Techniques

Abstract

Sentiment analysis, a key task in Natural Language Processing (NLP), aims to identify the underlying emotional tone of textual data. Although machine learning and deep learning techniques have achieved considerable success, these approaches often struggle with linguistic nuances such as sarcasm, negation, and syntactic ambiguity. Integrating linguistic knowledge—including syntax, semantics, and pragmatics—is essential for deeper sentiment interpretation; however, it remains underexplored in many sentiment analysis systems. This study presents a comprehensive review of sentiment analysis techniques that incorporate linguistic features to enhance accuracy and interpretability. We critically examine the roles of part-of-speech tagging (POS), dependency parsing, and discourse-level understanding, highlighting their contributions, limitations, and future potential. Furthermore, we discuss open challenges such as multilingualism and context-sensitive sentiment detection. By consolidating diverse linguistic strategies, this review provides a linguistically informed roadmap for advancing sentiment analysis, particularly in informal and complex language domains such as social media.