Hyperparameter elegance: fine-tuning text analysis with enhanced genetic algorithm hyperparameter landscape

Abstract

Due to the significant participation of the users, it is highly challenging to handle enormous datasets using machine learning algorithms. Deep learning methods are therefore designed with efficient hyperparameter sets to enhance the processing of the vast corpus. Different hyperparameter tuning models have been used previously in various studies. Still, tuning the deep learning models with the greatest possible number of hyperparameters has not yet been possible. This study developed a modified optimization methodology for effective hyperparameter identification, addressing the shortcomings of the previous studies. To get the optimum outcome, an enhanced genetic algorithm is used with modified crossover and mutation. The method has the ability to tune several hyperparameters simultaneously. The benchmark datasets for online reviews show outstanding results from the proposed methodology. The outcome demonstrates that the presented enhanced genetic algorithm-based hyperparameter tuning model performs better than other standard approaches with 88.73% classification accuracy, 87.31% sensitivity, 90.15% specificity, and 88.58% F-score value for the IMDB dataset and 92.17% classification accuracy, 91.89% sensitivity, 92.47% specificity, and 92.50% F-score value for the Yelp dataset while requiring less processing effort. To further enhance the performance, attention mechanism is applied to the designed model, achieving 89.62% accuracy, 88.59% sensitivity, 91.89% specificity, and 89.35% F-score with the IMDB dataset and 93.29% accuracy, 92.04% sensitivity, 93.22% specificity, and 92.98% F-score with the Yelp dataset.