DeepSeek-Generated Machine Learning Models for Sentiment Analysis in IoT Networks
Main Article Content
Abstract
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
Article Details
Upon receipt of accepted manuscripts, authors will be invited to complete a copyright license to publish the paper. At least the corresponding author must send the copyright form signed for publication. It is a condition of publication that authors grant an exclusive licence to the the INFOCOMP Journal of Computer Science. This ensures that requests from third parties to reproduce articles are handled efficiently and consistently and will also allow the article to be as widely disseminated as possible. In assigning the copyright license, authors may use their own material in other publications and ensure that the INFOCOMP Journal of Computer Science is acknowledged as the original publication place.
References
References
[1] Barbosa, R., Ogobuchi, O. D., Joy, O. O., Saadi, M., Rosa, R. L., Al Otaibi, S., and Rodríguez, D. Z. Iot based real-time traffic monitoring system using images sensors by sparse deep learning algorithm. Computer Communications, 210:321 330, 2023.
[2] Carrillo, D., Kalalas, C., Raussi, P., Michalopoulos, D. S., Rodríguez, D. Z., Kokkoniemi Tarkkanen, H., Ahola, K., Nardelli, P. H., Fraiden
raich, G., and Popovski, P. Boosting 5g on smart grid communication: A smart ran slicing approach. IEEE Wireless Communications,
30(5):170–178, 2022.
[3] Carvalho Barbosa, R., Shoaib Ayub, M., Lopes Rosa, R., Zegarra Rodríguez, D., and Wuttisittikulkij, L. Lightweight pvidnet: A priority vehicles detection network model based on deep learning for intelligent traffic lights. Sensors, 20(21):6218, 2020.
[4] de Sousa, A. L., OKey, O. D., Rosa, R. L., Saadi, M., and Rodriguez, D. Z. Unified approach to video-based ai inference tasks in augmented reality systems assisted by mobile edge computing. pages 1–5, 2023.
[5] dos Santos, M. R., Batista, A. P., Rosa, R. L., Saadi, M., Melgarejo, D. C., and Rodríguez, D. Z. Asqm: Audio streaming quality metric
based on network impairments and user preferences. IEEE Transactions on Consumer Electronics, 69(3):408–420, 2023.
[6] Fonseca, D., da Silva, K. C. N., Rosa, R. L., and Rodríguez, D. Z. Monitoring and classification of emotions in elderly people. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–6. IEEE, 2019.
[7] Matthew, U. O., Rosa, R. L., Kazaure, J. S., Adesina, O. J., Oluwatimilehin, O. A., Oforgu, C. M., Asuni, O., Okafor, N. U., and Rodriguez,
D. Z. Software-defined networks in iot ecosystems for renewable energy resource management. pages 1–5, 2024.
[8] Moriano, P., Hespeler, S. C., Li, M., and Mahbub, M. Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review. Artificial Intelligence Review, 58(9):1–46, 2025.
[9] Okey, O. D., Maidin, S. S., Adasme, P., Lopes Rosa, R., Saadi, M., Carrillo Melgarejo, D., and Zegarra Rodríguez, D. Boostedenml:
Efficient technique for detecting cyberattacks in iot systems using boosted ensemble machine learning. Sensors, 22(19):7409, 2022.
[10] Okey, O. D., Rodriguez, D. Z., and Kleinschmidt, J. H. Enhancing iot intrusion detection with federated learning-based cnn-gru and lstm-gru ensembles. In 2024 19th International Symposium on Wireless Communication Systems (ISWCS), pages 1–6. IEEE, 2024.
[11] Raghu, N., Mahesh, T., Vivek, V., Kumaran, S. Y., Kannanugo, N., and Vishwanatha, S. Iot-enabled safety and secure smart homes for elderly people. In Future of Digital Technology and AI in Social Sectors, pages 297–328. IGI Global, 2025.
[12] Ribeiro, D. A., Melgarejo, D. C., Saadi, M., Rosa, R. L., and Rodríguez, D. Z. A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5g and 6g network scenarios. Physical Communication, 56:101938, 2023.
[13] Ribeiro, D. A., Silva, J. C., Lopes Rosa, R., Saadi, M., Mumtaz, S., Wuttisittikulkij, L., Zegarra Rodriguez, D., and Al Otaibi, S. Light field
image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems. Electronics, 10(10):1136, 2021.
[14] Rodríguez, D. Z. and Möller, S. Speech quality parametric model that considers wireless network characteristics. In 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), pages 1–6. IEEE, 2019.
[15] Rosa, R. L., Rodriguez, D. Z., and Bressan, G.
Sentimeter-br: A social web analysis tool to discover consumers’ sentiment. In 2013 IEEE 14th international conference on mobile data management, volume 2, pages 122–124. IEEE, 2013.
[16] Salgado, J. V. T., Vinicius, D. Z. R., Dias, V. D. S., and Rosa, R. L. Automated validation of spatial data. pages 1–6, 2024.
[17] Saurabh, P. and Verma, B. An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Systems with Applications, 60:311–320, 2016.
[18] Silva, D. H., Maziero, E. G., Saadi, M., Rosa, R. L., Silva, J. C., Rodriguez, D. Z., and Igorevich, K. K. Big data analytics for critical
information classification in online social networks using classifier chains. Peer-to-Peer Networking and Applications, 15(1):626–641, 2022.
[19] Silva, D. H., Rosa, R. L., and Rodriguez, D. Z. Sentimental analysis of soccer games messages from social networks using userâs profiles. INFOCOMP JournalofComputerScience, 19(1), 2020.
[20] Teodoro, A. A., Gomes, O. S., Saadi, M., Silva, B. A., Rosa, R. L., and Rodríguez, D. Z. An fpga-based performance evaluation
of artificial neural network architecture algorithm for iot. Wireless Personal Communications, 127(2):1085–1116, 2022.
[21] Teodoro, A. A., Silva, D. H., Rosa, R. L., Saadi, M., Wuttisittikulkij, L., Mumtaz, R. A., and Rodriguez, D. Z. A skin cancer classification ap
proach using gan and roi-based attention mechanism. Journal of Signal Processing Systems, 95(2):211–224, 2023.
[22] Teodoro, A. A., Silva, D. H., Saadi, M., Okey, O. D., Rosa, R. L., Otaibi, S. A., and Rodríguez, D. Z. An analysis of image features extracted by cnns to design classification models for covid-19 and non-covid-19. Journal of signal processing systems, pages 1–13, 2023.
[23] Terra Vieira, S., Lopes Rosa, R., Zegarra Rodriguez, D., Arjona Ramírez, M., Saadi, M., and Wuttisittikulkij, L. Q-meter: Quality monitoring system for telecommunication services based on sentiment analysis using deep learning. Sensors, 21(5):1880, 2021.
[24] Xu, J., Guo, X., Zhang, Z., Liu, H., and Lee, C. Triboelectric mat multimodal sensing system (tmmss) enhanced by infrared image perception for sleep and emotion-relevant activity monitoring. Advanced Science, 12(6):2407888, 2025.
[25] Zhou, P., Ma, R., Chen, Y., Liu, Z., Liu, C., Meng, L., Qiao, G., Liu, Y., Yu, Q., and Hu, S. A neuromorphic transformer architecture
enabling hardware-friendly edge computing. IEEE Transactions on Circuits and Systems I: Regular Papers, 2025