Red Colobuses Monkey with Deep Recurrent Neural Network based Gender Classification on Health Web Forum Data

Computational Linguistics emphasizes the real-time consequences of modeling the utility of human languages. Applied Computational Linguistics can be utilized for several purposes in today's domain of scientific research and development, linguistic research, and business. Public utilizes health-based web forums to access health information easily. The government will operate medical web forums for providing medical data, managing needs and feelings of patients, and fostering information-sharing between patients. Gender classification is needed for automated identification of gender among people. This article presents a novel Deep Recurrent Neural Network for Computational Linguistics Driven Gender Classification (DRNNCL-GC) on Health Web Forums. The presented DRNNCL-GC technique is employed for the examination of health web forum data for gender recognition process. For attaining this, the DRNNCL-GC technique applies data preprocessing and feature engineering phases. Next, the DRNNCL-GC technique make use of DRNN model for gender classification process. Finally, Red Colobuses Monkey (RCM) algorithm is utilized for hyperparameter optimization process, which is stimulated by the characteristics of red monkeys in nature. The performance evaluation of the presented DRNNCL-GC technique is tested and the outcomes were measured under distinct aspects. A comprehensive comparison analysis highlighted the enhancements of the DRNNCL-GC technique over other recent approaches.