The dataset created focuses on the Pakistan Military by collecting five types of entities from Wikipedia: weapons, ranks, dates, operations, and locations. An open-source NER annotator was utilized for annotation, ensuring accurate labeling of data. Post-annotation, the data underwent cleaning and balancing processes. The final dataset comprises 660 neutral and 660 anti-military sentiment samples, totaling 1320 samples. This balanced dataset serves as a valuable resource for sentiment analysis, providing insights into public sentiment regarding military-related topics.

The massive damage caused by COVID-19 worldwide over the past two years has highlighted the importance of predicting the spread of infectious diseases. Therefore, with advances in deep learning, numerous and diverse methods have been considered for predicting the spread of infectious diseases. However, these studies have shown that the long-term prediction abilities of deep learning models are insufficient to predict the course and propagation of COVID-19 outbreaks.

The objective of this study is to conduct a systematic examination of research trends and hotspots in the domain of autonomous vehicles leveraging deep learning, through a bibliometric analysis. By scrutinizing research publications from various countries spanning 2017 to 2023, this paper aims to summarize effective research methodologies and identify potential innovative pathways to foster further advancements in AVs research. A total of 1,239 publications from the core collection of scientific networks were retrieved and utilized to construct a clustering network.