Natural language processing perspectives on emerging telecommunication networks

The telecommunications business contributes substantially to the growth of mobile communication and the information society. In recent years, the telecoms business has grown significantly. During lockdowns prompted by the COVID-19 epidemic, remote employees could continue operating their enterprises due to the accessibility of communications. This experiment corpus is taken from the Scopus database of 4776 articles from 2001 to March 2025. Topic modeling is the technique from Natural Language processing and in that Latent Dirichlet Allocation model is deployed on the considered corpus to predict research areas in the form of 2, 5, and 10 topics using k-mean clustering based on coherence score on the bag of words (BOW). The resulting clusters capture key research trajectories related to service performance and resource allocation, blockchain-enabled healthcare security and privacy, next-generation wireless communication technologies, antenna design and high-frequency prototyping, and smart city management and development. To strengthen traceability from empirical outputs to implications, the discussion is explicitly grounded in the LDA clusters through a structured mapping of topics to research questions and derived gaps, complemented by an adoption-oriented interpretation using TAM/UTAUT2 constructs. Based on these findings, a findings-derived research agenda is proposed, emphasizing energy-aware orchestration, scalable trust models, safe autonomous networking, robust THz/mmWave design, and governance-aware smart city infrastructures.