The accelerating growth of unstructured textual data across scientific, social, medical, and technological domains has created an unprecedented demand for intelligent systems capable of extracting meaningful, concise, and semantically rich information. Among these tasks, keyword extraction and semantic similarity modeling occupy a central role because they directly enable indexing, retrieval, sentiment analysis, document classification, question answering, and automated knowledge generation. Despite decades of research, the complexity of language, the heterogeneity of domains, and the short and noisy nature of many modern texts continue to challenge both classical and neural approaches. This study develops a comprehensive, integrative research framework that synthesizes classical keyword extraction algorithms, text similarity metrics, and state-of-the-art deep learning architectures to advance the theoretical and empirical understanding of automated semantic intelligence. Drawing strictly upon the referenced literature, this work connects statistical, graph-based, rule-based, and transformer-based approaches into a unified conceptual ecosystem.

The paper begins by positioning keyword extraction as a foundational problem in natural language engineering, grounded in issues of domain specificity, semantic ambiguity, and linguistic variability, as elaborated by Firoozeh et al. (2020) and Miah et al. (2021). Classical unsupervised techniques such as RAKE, SOBEK, and TextRank are explored not merely as algorithms but as epistemic devices that encode assumptions about term relevance, co-occurrence, and discourse structure (Huang et al., 2020; Reategui et al., 2022; Huang & Xie, 2021). These methods are then contrasted with modern deep learning paradigms such as BERT-based architectures and transformer-driven neural taggers, which model language through contextual embeddings and attention mechanisms that capture long-range semantic dependencies (Tang et al., 2019; Martinc et al., 2021).

A central theoretical contribution of this study is the articulation of text similarity as the conceptual bridge between keyword extraction, sentiment analysis, and semantic understanding. By integrating similarity measures such as Jaccard, embedding-based similarity, and semantic alignment, the article demonstrates how relevance, polarity, and conceptual cohesion can be modeled in a single analytical framework (Fernando & Herath, 2021; Mohler et al., 2011; Amur et al., 2023). The methodological section proposes a hybrid pipeline in which neural models generate contextual representations, while symbolic and statistical methods refine and validate extracted keywords against domain knowledge and prior public information (Huang & Xie, 2021; Jain et al., 2022).

The results are described through an extensive comparative analysis of how classical, hybrid, and deep learning approaches perform across domains such as scientific literature, social media, and biomedical texts. The findings show that deep neural architectures excel in capturing semantic nuance, while hybrid methods grounded in similarity metrics and domain knowledge improve precision, interpretability, and robustness to noise (Dang et al., 2020; Imran et al., 2020; Blake & Mangiameli, 2011). The discussion critically examines the implications of these findings for automated question generation, sentiment detection, and knowledge extraction in complex domains such as healthcare and education (Gilal et al., 2022; Alaggio et al., 2022).

By offering an integrated, theory-driven synthesis of keyword extraction, semantic similarity, and deep learning, this article contributes a comprehensive foundation for future research and applied systems. It argues that the future of text analytics lies not in the dominance of any single algorithmic paradigm, but in the strategic orchestration of symbolic, statistical, and neural intelligence into adaptive, explainable, and domain-aware semantic engines.