  Quantum kernels are considered as potential resources to illustrate benefits
of quantum computing in machine learning. Considering the impact of
hyperparameters on the performance of a classical machine learning model, it is
imperative to identify promising hyperparameters using quantum kernel methods
in order to achieve quantum advantages. In this work, we analyse and classify
sentiments of textual data using a new quantum kernel based on linear and full
entangled circuits as hyperparameters for controlling the correlation among
words. We also find that the use of linear and full entanglement further
controls the expressivity of the Quantum Support Vector Machine (QSVM). In
addition, we also compare the efficiency of the proposed circuit with other
quantum circuits and classical machine learning algorithms. Our results show
that the proposed fully entangled circuit outperforms all other fully or
linearly entangled circuits in addition to classical algorithms for most of the
features. In fact, as the feature increases the efficiency of our proposed
fully entangled model also increases significantly.
