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A Chatbot Intent Classifier for Supporting High School Students
dc.contributor.author | K. Assayed, Suha | |
dc.contributor.author | Shaalan, Khaled | |
dc.contributor.author | Alkhatib, Manar | |
dc.date.accessioned | 2025-05-14T09:19:53Z | |
dc.date.available | 2025-05-14T09:19:53Z | |
dc.date.issued | 2023-04-01 | |
dc.description.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. | |
dc.identifier.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. | |
dc.identifier.doi | https://doi.org/10.4108/eetsis.v10i2.2948. | |
dc.identifier.issn | 2032-9407 | |
dc.identifier.uri | https://bspace.buid.ac.ae/handle/1234/3011 | |
dc.language.iso | en_US | |
dc.publisher | EAI Endorsed Transactions on Scalable Information Systems | |
dc.relation.ispartofseries | ICST Transactions on Scalable Information Systems(20221221): e1 | |
dc.subject | intent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp | |
dc.title | A Chatbot Intent Classifier for Supporting High School Students | |
dc.type | Article |
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