International Journal on Information Technologies & Security, № 1 (vol. 13), 2021 89 AN APPLICATION METHOD OF LONG SHORT-TERM MEMORY NEURAL NETWORK IN CLASSIFYING ENGLISH AND TAGALOG-BASED CUSTOMER COMPLAINTS, FEEDBACKS, AND COMMENDATIONS Ralph Sherwin A. Corpuz Technological University of the Philippines e-mail: ralphsherwin_corpuz@tup.edu.ph Philippines Abstract: Classifying unstructured text data written in natural languages is a cumbersome task, and this is even worse in cases of vast datasets with multiple languages. In this paper, the author explored the utilization of Long Short-Term Neural Network (LSTM) in designing a classification model that can learn text patterns and classify English and Tagalog-based complaints, feedbacks and commendations of customers in the context of a state university in the Philippines. Results shown that the LSTM has its best training accuracy of 91.67% and elapsed time of 34s when it is tuned with 50 word embedding size and 50 hidden units. The study found that the lesser the number of hidden units in the network correlates to a higher classification accuracy and faster training time, but word embedding size has no correlation to the classification performance. Furthermore, results of actual testing proven that the proposed text classification model was able to predict 19 out of 20 test data correctly, hence, 95% classification accuracy. This means that the method conducted was effective in realizing the primary outcome of the study. This paper is part of a series of studies that employs machine and deep learning techniques toward the improvement of data analytics in a Quality Management System (QMS). Key words: Long Short-Term Memory, Deep Learning, Neural Network, Text Classification, Natural Language Processing, Customer Satisfaction 1. INTRODUCTION In the era of the so-called Fourth Industrial Revolution (4IR), organizations are constantly challenged to remain relevant with their visions and missions, particularly in consistently delivering quality products and services. This challenge is likewise evident in the case of state universities, wherein the satisfaction of students, parents, research and industry partners, including the regulatory bodies are considered crucial toward effective management of their core and support processes and toward the