High-Resolution NLP for Social Healthcare Networks: Text Classification through Integration of Causal Dilated Cosine Architecture Optimized by Weighted Leadership Navigator for Advanced Insights

The advancement in information technology has been on the increase in the recent past with the expansion of text data dissemination in the form of news, medical reports, product reviews, and social media posts among others, the increase in text data has made text mining and classification mandatory. In the last decade a lot of attempts have been undertaken to solve the text classification problems employing machine learning and deep learning approaches both supervised and unsupervised. In this context, our research proposes High-Resolution NLP for Social Healthcare Networks (HiN-TC), a state-of-the-art text classification approach. In our research, data cleaning and normalization are done using Min-Max with Zero-Mean Rescaling Normalization, feature selection is done using Echelon Adaptive Poplar Algorithm, and feature extraction is done using Bi-Consolidation Transformer that captures key patterns in the text. It is supposed to classify the data with good accuracy using Causal Dilated Cosine Architecture and we have optimized it using Precision-Weighted Leadership Navigator for improved performance. This framework is applied to the COVID-19 vaccination Twitter dataset to help get more superior understanding of social healthcare trends. Our methodology demonstrates significant performance improvements, achieving a 98.5% increase in classification accuracy, a 96.3% reduction in text processing time, and a 97.1% boost in scalability. These outcomes suggest the capability of the HiN-TC to deliver a more profound understanding of the social healthcare networks and suggest that the presented framework can be effective for the enhancement of the efficiency and accuracy in the computationally extensive range of the NLP methods and applications for the analysis of the large-scale unstructured text data in healthcare.