SentiNet: A Deep Learning-Based Architecture with Hyperparameter Optimization for Sentiment Analysis of Customer Feedback Reviews

In the contemporary era, customers' opinions are paramount as they can help improve the quality of service that leads to business growth. People from all walks of life often voice their opinions about goods and services using social media. Modern businesses cannot ignore social media feedback. As technology advances, including big data, cloud computing, and distributed computing architectures, it is now possible to analyze large volumes of data to discover trends or patterns in customer behavior. Therefore, sentiment analysis has become an important research area that helps organizations promote their businesses. Artificial intelligence, of late, is widely used to discover sentiments from customer reviews. However, analyzing sentiments accurately is nontrivial due to the complexity of procedures involved in mining textual data. There is a need to leverage performance in sentiment analysis. This research aimed to achieve this goal by proposing a DL-based framework for analyzing the sentiments of given data. We proposed a novel DL architecture, SentiNet, to efficiently classify sentiments in customer reviews. We proposed an Efficient Learning-Based Sentiment Analyzer (LBSA) algorithm, which exploits novel vectorization, embeddings, and the novel architecture of the proposed SentiNet model. Our empirical study with a benchmark dataset of customer reviews on restaurants and food items revealed that the proposed SentiNet model outperformed many existing DLs with the most accurate simulations of 98.68%. Our framework can be incorporated into business applications for sentiment analysis and improving service quality