AI-Enabled Sentiment Analysis for Strategic Content Curation

Abstract

The following paper presents a content curation sentiment detection model based on AI, and why it is crucial to protect the user data during the analysis. The model employs the existing technologies of natural language processing (NLP) and machine learning to analyze the content that is created by users during a social media post, review, and comment. The model classifies sentiments as positive, neutral, and negative and proposes individual content suggestions depending on the created users' interests. In addition, the model has implemented data protection policies which include data encryption, role-based access control (RBAC), as well as safe data scraping APIs to access the social media and review sites. Such data protection policies will guarantee privacy and integrity of user data and hence reduce the possibilities of unauthorized access, breach, and exploitation. The ability of the model to be adjusted to the evolving feelings of the users, together with the security measures, will provide a secure and ethical alternative to content curation. The results of the pilot research indicate that there is an indication of a large positive effect of the sentiment-analysis attention-based system on both engagement and post-content engagement outcomes. This paper will discuss the affordances of AI as a means of developing responsive, user-friendly content to be used in the work of a curator by designing AI security, privacy, and personalization of the content. Findings offer proof of the worth of secure systems installed to prevent adversarial assaults and information manipulation to make a safe, dependable system application of AI in content aggregation.