Special Issue on Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis

1. Introduction

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.

2. Overview of Published Articles

As previously mentioned, this special issue brings together a comprehensive collection of articles that reflect the latest advancements in nlp, semantic networks, and sentiment analysis. The contributions represent a confluence of theoretical innovation and practical application, offering a panoramic view of current research trends and future directions in the field.

One of the central themes emerging from this volume is the integration of deep learning techniques with domain-specific tasks such as video-based sentiment analysis, multilingual offensive language detection, and tourism-oriented opinion mining. A noteworthy example is the application of dynamic scene segmentation combined with emotional classification in the context of Danmaku comments. By leveraging Deep Convolutional Neural Networks for shot segmentation and fine-tuned language models, such as MacBERT, for emotion analysis, this study demonstrates how context-aware architectures can significantly enhance the interpretability and accuracy of affective computing in multimedia environments. This fusion of visual and textual modalities is representative of the broader trend toward multimodal understanding in nlp.

Bias in sentiment analysis, particularly in social media datasets, constitutes another critical concern explored in this issue. The introduction of a classifier designed with fairness-aware loss functions exemplifies how algorithmic interventions can mitigate the distortions introduced by unbalanced training data. The ethical commitment reflected in this framework underscores a growing consensus in the nlp community regarding the need for socially responsible artificial intelligence systems that are not only effective but also equitable.

Equally compelling is the attention to underrepresented linguistic contexts. The challenge of detecting offensive language in low-resource languages, such as Malay, is addressed with a novel zero-shot, cross-lingual model based on mBERT. By aligning semantic features across languages and deploying a dual-branch training mechanism, the approach effectively transfers knowledge from high-resource languages, such as English. This direction is crucial for democratizing nlp technologies and ensuring inclusivity in their development and deployment.

In the domain of sentiment analysis applied to tourism, a study using user reviews from Tripadvisor demonstrates how fine-tuned RoBERTa models can capture subtle changes in visitor sentiment across time. By incorporating data augmentation to address class imbalances and conducting temporal comparisons, the work not only validates the technical robustness of the model but also provides actionable insights for urban and tourism planning.

Moreover, hate speech and offensive language, particularly when veiled in sarcasm or disguised through emoticons, pose persistent challenges to content moderation. A creative solution offered in this issue involves annotating sarcasm-based rationales in combination with hate speech labels to train more interpretable and practical models. By enriching existing datasets and introducing rationale-based attention mechanisms, this approach significantly improves both classification accuracy and model explainability.

On the other hand, several contributions in this issue delve into the foundations of nlp systems, focusing on robustness, representation, and reasoning. A comprehensive survey on adversarial attacks against sentiment analysis models exposes the vulnerability of deep learning systems to small perturbations in input text. This survey not only catalogs current attack strategies but also discusses defense mechanisms, offering a roadmap for enhancing model resilience in real-world applications.

Knowledge representation and reasoning also take center stage, with two sophisticated approaches aimed at completing and reasoning over knowledge graphs. One model enhances entity embeddings by fusing categorical metadata and textual context through multi-layer residual attention, significantly improving link prediction accuracy. Another introduces a causal reinforcement learning framework that integrates counterfactual reasoning to prioritize relationship paths in graph traversal. Together, these contributions constitute a leap forward in enabling machines to reason over structured knowledge with greater nuance and context sensitivity.

In terms of relation extraction, the issue includes an innovative framework that breaks down the task into modular subtasks using entity attention mechanisms and a cascade tagging structure. This design directly addresses the long-standing problem of overlapping relations in the text of traditional extraction models. By isolating head and tail entity identification, the solutions achieve superior granularity and precision, validated through rigorous experiments on Chinese-language datasets.

On the unsupervised learning field, another contribution introduces prefix-based data augmentation for contrastive sentence embedding. By appending semantically meaningful prefixes, the model generates more informative positive and negative pairs, resulting in measurable gains over standard unsupervised methods. This innovation illustrates the potential of minimal but strategic data manipulation to enhance semantic representation learning.

Finally, the issue underscores the importance of human-centric design in assistive technologies. An analysis of interaction logs from users of the PictoDroid Lite communicator reveals critical usability patterns and areas for improvement, including personalized profiling and temporal usage optimization. This research brings a practical, user-focused dimension to nlp research.

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.

Furthermore, there is growing interest in extending these approaches to new domains. In the case of sentiment analysis applied to tourism, the methodology is expected to be scaled to other tourist destinations, aiming to contribute to feasibility studies in sustainability and strategic planning. In parallel, emerging applications in business process intelligence are explored, an area in which models can add value by interpreting, monitoring, and optimizing business workflows based on linguistic data.

From an architectural perspective, future research will consider the use of large language models, whose integration will enable the exploration of higher levels of generalization and inferential capabilities. Although the computational demands of these models have limited their use in previous studies, their progressive incorporation opens a field for comparing the effectiveness of various architectures and better understanding the model’s adaptability to different linguistic and semantic contexts. Similarly, the use of ensemble techniques and graph neural networks is proposed to enhance the representation of entities and relationships, particularly in tasks such as link prediction in knowledge graphs and the extraction of complex relationships.

Particularly in the field of knowledge graphs, designing better negative sampling strategies and reducing model time complexity by replacing attention mechanisms without sacrificing performance are considered priorities. Contextual information is also planned to be integrated through graph neural networks to improve the quality of inferences and the semantic contextualization of relationships. Moreover, in relation extraction tasks, a shift toward hybrid systems that combine architectures with large language models is anticipated. This integration will allow the injection of prior knowledge about the extracted entities, improving the assignment of attention weights and the identification of complex or implicit relationships.

Finally, the design of defense mechanisms against adversarial attacks should be oriented toward solutions that are independent of the type of attack, incorporating them as an integral part of the models from their inception. Ensuring data integrity and the stability of predictions against external manipulation will be key to the trustworthiness and practical utility of systems in areas such as public opinion, mental health, justice, and decision-making.