An Optimized Deep Neural Aspect Based Framework for Sentiment Classification

The current digitalized world has emerged with the advances of big data, such as online learning, business, marketing, etc. So reviewing the customer opinion in each field is more critical in enabling smart services. Several neural models are implemented diversely to characterize the customer opinion from the gained review of the user or customer. But, those models have met some difficulties detecting the aspect terms and sentiment values. So, this proposed article has planned to design a novel Strawberry Deep Belief Neural Mechanism (SDBNM) to classify the aspect and sentiment values from the trained datasets. In addition, to evaluate the performance of the designed approach, three datasets are adopted: product, trip review, and Twitter data. Initially, after the data training process, the error is extracted from the database in the preprocessing model of SDBNM. Hereafter, the error-free data is imported to the dense frame of the SDBNM to specify the aspect terms and sentiment values. At last, the parameters are estimated and compared with old approaches and have earned the best results for classification rate.