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A Chatbot Intent Classifier for Supporting High School Students
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EAI Endorsed Transactions on
Scalable Information Systems
Abstract
INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking
the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an
essential role in having a chatbot conversation that understand students’ requests.
OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications
from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about
their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their
career choice.
METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the
students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions.
RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the
performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all
showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier
performed much better when using CountVectorizers compared to using the TF-IDF.
CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors
that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large
corpus of students’ questions and enquiries.
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Citation
Assayed, S.K., Shaalan, K. and Alkhatib, M. (2022) “A Chatbot Intent Classifier for Supporting High School Students,” ICST Transactions on Scalable Information Systems, p. e1.