A Text Classification Approach Based on Natural Language Processing and Machine Learning Techniques

Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

Abstract:

Automatic text classification applies mostly natural language processing (NLP) and other artificial intelligence (AI)-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: Machine learning, text classification, NLP techniques, semantic representation.

References:

[1] Li, Y. (2022, January). Research and application of deep learning in image recognition. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) (pp. 994-999). IEEE.
[2] Roy, S. D., & Debbarma, S. (2022). A novel OC-SVM based ensemble learning framework for attack detection in AGC loop of power systems. Electric Power Systems Research, 202, 107625.
[3] Barbariol, T., & Susto, G. A. (2022). TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios. Information Sciences, 610, 126-143.
[4] Bekar, E. T., Nyqvist, P., & Skoogh, A. (2020). An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Advances in Mechanical Engineering, 12(5), 1687814020919207.
[5] Obermair, C., Apollonio, A., Wuensch, W., Felsberger, L., Cartier-Michaud, T., Catalán Lasheras, N., ... & Millar, W. L. (2021). JACoW: Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators. JACoW IPAC, 2021, 1068-1071.
[6] Malakouti, S. M., Ghiasi, A. R., Ghavifekr, A. A., & Emami, P. (2022). Predicting wind power generation using machine learning and CNN-LSTM approaches. Wind Engineering, 46(6), 1853-1869.
[7] Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187-3191.
[8] Nuankaew, P., Chaising, S., & Temdee, P. (2021). Average weighted objective distance-based method for type 2 diabetes prediction. IEEE Access, 9, 137015-137028.
[9] Oza, P., & Patel, V. M. (2019, May). Active authentication using an autoencoder regularized cnn-based one-class classifier. In 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) (pp. 1-8). IEEE.
[10] Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press.
[11] Sharifani, K., Amini, M., Akbari, Y., & Aghajanzadeh Godarzi, J. (2022). Operating Machine Learning across Natural Language Processing Techniques for Improvement of Fabricated News Model. International Journal of Science and Information System Research, 12(9), 20-44.
[12] Öztürk, E., Solak, A., Bäcker, D., Weiss, L., & Wegener, K. (2022). Analysis and relevance of service reports to extend predictive maintenance of large-scale plants. Procedia CIRP, 107, 1551-1558.
[13] Carchiolo, V., Longheu, A., Di Martino, V., & Consoli, N. (2019, September). Power Plants Failure Reports Analysis for Predictive Maintenance. In WEBIST (pp. 404-410).
[14] Usuga-Cadavid, J. P., Lamouri, S., Grabot, B., & Fortin, A. (2021). Using deep learning to value free-form text data for predictive maintenance. International Journal of Production Research, 1-28.
[15] Gasparetto, A., Marcuzzo, M., Zangari, A., & Albarelli, A. (2022). A Survey on Text Classification Algorithms: From Text to Predictions. Information, 13(2), 83.
[16] Mehta, R., Jurečková, O., & Stamp, M. (2023). A Natural Language Processing Approach to Malware Classification. arXiv preprint arXiv:2307.11032.