Using Sentiment Analysis and Knowledge Discovery System as a Tool to Enhance a Real-Time Product Evaluation System

The explosion of user-generated content on platforms such as social media and review sites has created a wealth of data that businesses can leverage to understand consumer sentiment. This paper explores the integration of sentiment analysis and knowledge discovery systems to enhance real-time product evaluation. By utilizing advanced machine learning techniques, particularly Naïve Bayes and Recurrent Neural Networks (RNNs), the proposed system aims to provide deeper insights into customer feedback and improve decision-making processes in businesses. The findings suggest that the hybrid model significantly enhances accuracy and responsiveness in product evaluations, ultimately leading to better customer satisfaction and competitive advantage.

Sentiment Analysis, Real-Time, Machine Learning

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