###### Abstract  
In recent years, natural language processing (NLP) has undergone a profound transformation driven by significant advances in artificial intelligence, machine learning, and the massive availability of linguistic data [1]. Language, an essential tool for reasoning and emotional expression, can be leveraged to equip machines with the ability to understand, generate, and analyze it. The latter is one of the most ambitious and far-reaching challenges in contemporary computational science [2].  
This special issue aims to bring together recent research, innovative methodological proposals, and emerging applications that contribute to the development of more efficient, accurate, and contextually aware language technologies. The convergence of NLP, semantic networks, and sentiment analysis enables an approach to complex problems related to semantic ambiguity, contextual inference, subjectivity detection, and the interpretation of implicit meanings [3], thereby opening up new possibilities for deep natural language understanding.  
NLP encompasses a wide range of tasks, including text segmentation, morpho-syntactic tagging, syntactic and semantic analysis, lexical disambiguation, automatic language generation, machine translation, information extraction, and entity recognition [4]. The rise of deep neural networks, particularly language models based on transformer architectures such as BERT, GPT, and their derivatives, has revolutionized these processes, enabling unprecedented levels of performance across multiple languages and specialized domains [5].  
In parallel, semantic networks have reemerged as a crucial component in representing linguistic and conceptual knowledge [6]. These structures, which model entities and relationships through graphs, facilitate the integration of linguistic and ontological data, enabling a more structured and reasoned interpretation of language. Computational semantics, supported by resources such as WordNet, BabelNet, and multilingual knowledge bases, is articulated with NLP models to enrich text understanding with contextual and conceptual information.  
Additionally, sentiment analysis has established itself as a fundamental discipline for evaluating opinions and emotions expressed in texts [7]. This area is especially relevant in contexts such as social network analysis, product and service feedback, media monitoring, the study of political discourse, and the early detection of psychosocial risks [8]. Through supervised learning techniques, lexicographic models, and hybrid approaches, sentiment analysis enables the distinction between positive, negative, and neutral expressions, as well as the capture of more subtle affective nuances.  
In short, this special issue examines the intersection of NLP, semantic networks, and sentiment analysis, exploring how these technologies can contribute to more intelligent systems. The contributions gathered address, among other topics, the detection of irony and sarcasm, the generation of semantic explanations, the automatic construction of semantic graphs, the expansion of linguistic resources for underrepresented languages, and the use of large language models. Ultimately, this special issue aims not only to reflect the state of the art in the areas above but also to stimulate critical reflection on the methodological challenges, ethical implications, and interdisciplinary opportunities that arise at the intersection of computational linguistics and artificial intelligence.