A Novel Deep Belief Neural Framework Based on Ant Colony to Analyze the Sentiment Score in Drug Review Dataset

The sentiment classification in big data is the critical prerequisite for all applications. Because handling huge data for all applications is a difficult undertaking, the execution process takes longer. Additionally, the big data of today governs the entire advanced globe through its applications and social media platforms. Therefore, some machine learning models are used to manage online services more effectively. Machine learning approach is also becoming a popular area in big data; practically all drug review datasets contain positive, negative, and neutral sentiment values. The sentiment analysis value prediction plays a significant role in the marketing system and product development areas. In order to quantify the sentiment value of online medicine reviews, and unique Ant Colony based Deep Belief Neuronal (AC-DBN) framework has been developed. Additionally, the preprocessing layer trains on the acquired datasets. As a result, the ant colony model's fitness function in the boosting strategy enhances the precision and accuracy of opinion specification while also simplifying the classification procedure. The new system's results are sequentially compared to those of the present technique in terms of accuracy, precision, recall, aspect term specification, and opinion condition, among other metrics. A higher accuracy and precision of opinion specification led to a better outcome.