A Chatbot Intent Classifier for Supporting High School Students

dc.contributor.authorK. Assayed, Suha
dc.contributor.authorShaalan, Khaled
dc.contributor.authorAlkhatib, Manar
dc.date.accessioned2025-05-14T09:19:53Z
dc.date.available2025-05-14T09:19:53Z
dc.date.issued2023-04-01
dc.description.abstractINTRODUCTION: 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.citationAssayed, 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.doihttps://doi.org/10.4108/eetsis.v10i2.2948.
dc.identifier.issn2032-9407
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3011
dc.language.isoen_US
dc.publisherEAI Endorsed Transactions on Scalable Information Systems
dc.relation.ispartofseriesICST Transactions on Scalable Information Systems(20221221): e1
dc.subjectintent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp
dc.titleA Chatbot Intent Classifier for Supporting High School Students
dc.typeArticle

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