Ontology-Based Naive Bayes Short Text Classification Method for a Small Dataset

Content less than two hundred words like comments or review statements is known as a short text. Short text classification is useful for automatically categorizing sentence into predefined group. There are several traditional short text classification methods by using bag-of-words with k nearest neighbors (k-NN), Naive Bayes, Maximum entropy, support vector machines (SVMs), and an algorithm based on statistics and rules. The deep learning method is outperformed other methods on classification of short text with normal size of dataset. Some researches classify requirements into functional and non-functional requirements. There is no research on multiclassification of functional requirements with a small dataset particularly for an accounting field. This paper presents an approach to classify short text for a small dataset into multiple categories of functional requirements on the accounting domain. The proposed approach uses an ontology to construct bag-of-words and uses Naive Bayes to classify for small dataset. The experiment is conducted using four hundred of datasets with 5-folds and 10-folds cross validation. The result shows that the method can correctly classify more than 80%. Additionally, comparisons between the ontology-based Naive Bayes method and other methods are investigated.