Modeling a Functional Engine for the Opinion Mining as a Service using Compounded Score Computation and Machine Learning

Abstract

The ever-growing use of the digital platform for the various walks of the applications, primarily on the collaborative platforms of e-commerce, e-learning, social media, blogging, and many more, produces a large corpus of unstructured text data. Many potential strategic solutions require an accurate and fast classification process of the Opinion's text corpus hidden patterns. In-premise applications have various real-time feasibility constraints. Therefore, offering an Opinion as a Service on the cloud platforms is a new research domain. This paper proposes a design framework of the evolution of the classification engine for opinion mining using score-based computation using a customized Vader algorithm. Another method for scalability is a machine learning model that supports a large corpus of unstructured text data classifications. The model validation is performed for the various complexes, unstructured text datasets with the different performance metrics of the cumulative score, learning rate, loss function, and specificity analysis. These metrics indicate the models' stability and scalability behaviors and their accuracy and robustness across different datasets.