Natural language processing (NLP) aims to analyze a large amount of natural language data. The NLP computes textual data via a set of data processing elements which is sequentially connected to a path data pipeline. Several data pipelines exist for a given set of textual data with various degrees of model accuracy. Instead of trying all the possible paths, such as random search or grid search to find an optimal path, we utilized the Bayesian optimization to search along with the space of hyper-parameters learning. In this study, we proposed a ...