AI-based sentiment analysis approaches for large-scale data domains of public and security interests

Abstract

Organizational service learn-leadership design for adapting and predicting machine learning-based sentiments of sociotechnical systems is being addressed in segmenting textual-producing agents in classes. In the past, there have been numerous demonstrations in different language models (LMs) and (naıve) Bayesian Networks (BN) that can classify textual knowledge origin for different classes based on decisive binary trees from the future prediction aspect of how public text collection and processing can be approached, converging the root causes of events. An example is how communication influence and affect the end-user. Within service providers and industry, the progress of processing communication relies on formal clinical and informal non-practices. The LM is based on handcrafted division on machine learning (ML) approaches representing the subset of AI and can be used as an orthogonal policy-as-a-target leadership tool in customer or political discussions. The classifiers which use the numeric representation of textual information are classified in a Neural Network (NN) by characterizing, for instance, the communication using cross-sectional analysis methods. The textual form of reality collected in the databases has significant processable value-adding opportunities in different management and leadership, education, and climate control sectors. The data can be used cautiously for establishing and maintaining new and current business operations and innovations. There is currently a lack of understanding of how to use most NN and DN methods. The operations and innovations management and leadership support the flow of communication for effectiveness and quality.