A weighted metric scalarization approach for multiobjective BOHB hyperparameter optimization in LSTM model for sentiment analysis

In recent years, hyperparameter tuning has become increasingly essential, especially for neural network models, where models with good performance generally require a long time to train. In contrast, fast-trained models often result in poor model performance. Thus, it is essential to determine the optimal hyperparameter configuration for a neural network model that can produce good model performance and fast training time. This paper presents a multiobjective optimization for Long-Short Term Memory (LSTM) model using Bayesian optimization-Hyperband (BOHB) with a weighted metric scalarization method. The objective functions used are the F1-score (Macro) and model training time, where both of the objective functions are considered equally important. The data used in this paper are the SmSA and EmoT datasets taken from the IndoNLU benchmark. The results are optimized model using multiobjective BOHB optimization with weighted metric scalarization method produced higher F1-score and faster model training time in both datasets compared to the baseline model and optimized model using single objective BOHB.