| Abstrakt: |
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) [ABSTRACT FROM AUTHOR] |
