Quantum Natural Language Processing (QNLP) stands at the forefront of interdisciplinary research, amalgamating quantum computing principles with natural language processing techniques to revolutionize sentiment analysis (SA). Departing from traditional methods grounded in classical computation, QNLP holds promise for surmounting specific challenges within SA, offering a novel lens through which to discern the intricacies of human emotions, attitudes, and assessments in textual data. A burgeoning body of research underscores the efficacy of quantum NLP in augmenting SA performance, underscoring its potential to surpass classical approaches in capturing the nuances of human sentiment. In the contemporary landscape, quantum cognitive-inspired models have emerged as formidable tools for SA, demonstrating superior efficacy compared to their classical counterparts. This chapter embarks on a rigorous exploration of QNLP, commencing with a meticulous examination of quantum probability and cognition. By elucidating the advantages afforded by these quantum concepts over classical cognitive-based SA theories, we lay the groundwork for understanding the transformative potential of QNLP. Subsequently, we delve into various approaches devised to tackle the inherent challenges of SA tasks, showcasing how quantum techniques can furnish innovative solutions. From quantum-enhanced feature extraction to sophisticated sentiment classification algorithms, we scrutinize the evolving landscape of SA methodologies enriched by quantum principles. Moreover, this chapter critically evaluates the current state of QNLP research, acknowledging both its achievements and limitations. While quantum-inspired models have shown remarkable promise, practical constraints and theoretical boundaries necessitate further investigation to unlock their full potential. By illuminating these nuances, we aim to foster a deeper understanding of QNLP’s complexities and inspire future research endeavors aimed at refining SA techniques for greater precision and insight.