DeepSeek-Generated Machine Learning Models for Sentiment Analysis in IoT Networks - Begazo, Dante Coaquira; OYEDEMI, OLUYEMISI; Yetunde Esther, Ogunwale (INFOCOMP Journal of Computer Science, 2025)
DeepSeek, an advanced AI model for code generation and data processing, has shown sig nificant potential in automating the development of machine learning models. This paper reviews a DeepSeek-generated machine learning model for sentiment analysis in Internet of Things (IoT) net works, focusing on its architecture, implementation, and performance metrics. The model, designed for classifying textual data from IoT devices, leverages a transformer-based architecture with hierar chical clustering for efficient data processing. The review examines the code structure, algorithmic efficiency, and quantitative performance metrics such as accuracy, precision, and computational com plexity. Comparative analysis with traditional machine learning approaches, including Support Vector Machines (SVM) and Random Forests, is provided based on standard datasets. The results indicate that the DeepSeek-generated model achieves competitive performance while reducing development time. Po tential improvements, such as incorporating advanced feature engineering and multi-modal data integra tion, are suggested for future enhancements. This review highlights DeepSeekâs capability to streamline machine learning model development for IoT applications. Keywords: DeepSeek, Machine Learning, Sentiment Analysis, IoT Networks, Transformer Models