A Deep Learning-Based Enhanced Sentiment Classification and Consistency Analysis of Queries and Results in Search Using Oracle Hybrid Feature Extraction - Muhammad Yousaf; Farwa Khalid; Muhammad Usman Saleem; Mohe Ud Din; Abdul Karim Shahid; Hamayun Khan (Spectrum of Engineering Sciences, 2025)
Multiple assessments of customer feedback play a vital role in the industry because they help enhance product quality while spotting major network issues and creating improved customer-facing services. A traditional sentiment analysis process relies on external machine learning frameworks that result in system integration issues and performance reduction because large data volumes transfer between different options such as API and FTP file sharing before applying data machine learning models that extract insights. This paper proposed a Machine learning-based Model based on (CNN, RNN, and DT Classifier) and focuses on extracting sentiment metadata that links to the user-selected topic or entity together with their search results. The real-time sentiment analysis system which operates in Oracle Autonomous Database uses OML4SQL and OML4PY components from Oracle Machine Learning to process Communication customer feedback obtained through the web-based Oracle APEX system. The predictive model developed by the research utilizes CNNs and RNN algorithms provided by Oracle to identify whether customer reviews are positive, negative, or neutral. After receiving training the model functions to classify fresh feedback immediately while bypassing dependencies on external AI platforms. The implementation occurs inside the Autonomous Oracle Database while bypassing API or FTP file-sharing methods. The analysis reveals OML4SQL and OML4PY succeed in customer sentiment analysis thus enabling Software organizations to acquire valuable business information for better service delivery and strategic choices. The findings from this research demonstrate that the Convolutional Neural Network (CNN) achieved the highest accuracy (92.5%), followed by RNN (90.2%), while DT (85.4%) performed relatively lower. The analysis of 15,500 customer reviews revealed that 48.1% were positive, 39.4% were negative, and 33.7% were neutral. Oracle machine learning tools (ML4SQL and OML4PY) provide real-time text analytics in Software databases which enables service-based decisions through automated sentiment analysis technology within customer support operations. Keywords: Sentiment Analysis, Oracle Machine Learning for SQL (OML4SQL), Oracle Machine Learning for Python (OML4PY), Machine Learning.