## 3 Conclusions and Future Work  
The papers gathered in this special issue reflect the current state of research in NLP, semantic networks, and sentiment analysis, demonstrating a clear evolution toward more integrative, robust, ethical, and adaptable approaches. Through a diversity of methodologies, languages, application domains, and data types, the articles compiled demonstrate the dynamism and transversality of these disciplines in a global and digitalized context. First, there is a trend toward the hybridization of techniques, such as combining deep learning models with structured semantic representations and causal inference frameworks. This multidimensional approach enables the tackling of complex problems, such as the emotional segmentation of multimedia content, relationship prediction in knowledge graphs, and the detection of offensive speech in multilingual contexts, with results significantly superior to those of traditional methods. Likewise, the growing attention to under-resourced languages and less-studied cultural contexts, such as Malay in social media or tourism analysis in Dubrovnik, highlights the need to build more inclusive language technologies, adapted to diverse linguistic and social realities. These initiatives not only promote equity in access to technological solutions but also enrich models with a plurality of data and expressions. Another cross-cutting theme is the concern for the fairness, explainability, and resilience of models. Faced with the ethical and technical challenges posed by algorithmic biases, adversarial attacks, and subtle forms of online toxicity, several works propose corrective mechanisms and diagnostic tools that strengthen the trustworthiness of systems. These advances contribute to a more responsible artificial intelligence, where transparency and fairness are not complementary, but fundamental requirements. Furthermore, this issue also highlights an orientation toward user-centered design, as exemplified by the study of augmentative and alternative communication technologies. The analysis of fundamental interactions allows us to identify specific needs and guide the development of more sensitive, personalized, and practical solutions. However, the work presented in this special issue opens up a wide range of opportunities for future lines of research, both in terms of technical improvements and their social impact and adaptability to new contexts. Despite the substantial advances demonstrated in language modeling, sentiment analysis, semantic representation, and automated reasoning, significant challenges remain that require more sophisticated methodologies, alternative architectures, and collaborative approaches. One of the main challenges identified is the need to improve models' ability to handle mixed or ambiguous emotions. Human emotions are rarely expressed univocally, and developing models capable of capturing this complexity is a crucial objective for achieving a more nuanced understanding of language. In this regard, the continuous expansion and updating of affective lexicons is proposed, as well as the incorporation of feedback mechanisms from human users or experts to validate and adjust model predictions in real-world settings. This feedback loop will not only contribute to better calibration of systems but also to the refinement of bias mitigation techniques, strengthening the fairness and robustness of the analysis in diverse contexts. Another key area will be the expansion and diversification of the datasets used to train and validate the models. As languages and social norms evolve, linguistic models will need to adapt dynamically, which requires updated, representative, and culturally contextualized corpora. In parallel, improving scene segmentation in multimodal analysis, enhancing the computational efficiency of models, and designing defense strategies against adversarial attacks are priority areas to ensure system performance and security in critical applications.