## 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.