Mounting-based knowledge transfer network Model Using Aspect-based sentiment analysis

Abstract

Abstract Aspect-based sentiment analysis is a complex subtask of sentiment analysis in which the sentiment polarities of given aspect terms in phrases are determined. In this paper, we suggested a novel Mounting-based knowledge transmission network for aspect-based sentiment analysis. First and foremost, before entering data into models, data preparation (data pre-processing) is crucial, as it contributes to achieving high accuracy. For the textual data, special procedures were required, including tokenization, stop word deletion, and the Weighting Factor, which was all applied in this study. Second, employing feature extraction based on the Aspect term using NB, Modified BERT (MBERT) together with Entropy measure may focus on the critical context words to the given aspect terms in sentences. Finally, Convolution neural network is used to predict the result of sentiment classification aspect polarities as positive or negative.Experimental results show that our proposed approach accomplishes the most extreme accuracy outcome of 92%. Comparable to the TMSC and DNN, which achieves 78%, 72%. The MGAN and CNN had the accuracyworst of 70% and 71%. The HSCN had the very worst accuracy69%.