A Chatbot Intent Classifier for Supporting High School Students
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 understan...
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2022
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3009 |
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| _version_ | 1862980618973872128 |
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| author | K. Assayed, Suha |
| author2 | Shaalan, Khaled Alkhatib, Manar |
| author2_role | author author |
| author_facet | K. Assayed, Suha Shaalan, Khaled Alkhatib, Manar |
| author_role | author |
| dc.creator.none.fl_str_mv | K. Assayed, Suha Shaalan, Khaled Alkhatib, Manar |
| dc.date.none.fl_str_mv | 2022 2025-05-14T09:19:40Z 2025-05-14T09:19:40Z |
| dc.identifier.none.fl_str_mv | https://bspace.buid.ac.ae/handle/1234/3009 |
| dc.language.none.fl_str_mv | en |
| dc.title.none.fl_str_mv | A Chatbot Intent Classifier for Supporting High School Students |
| dc.type.none.fl_str_mv | Article |
| description | 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. |
| id | budr_9e5769e7482fbcf8279344da64ef382a |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3009 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Chatbot Intent Classifier for Supporting High School StudentsK. Assayed, SuhaShaalan, KhaledAlkhatib, ManarINTRODUCTION: 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.2025-05-14T09:19:40Z2025-05-14T09:19:40Z2022Articlehttps://bspace.buid.ac.ae/handle/1234/3009enoai:bspace.buid.ac.ae:1234/30092025-05-14T09:19:42Z |
| spellingShingle | A Chatbot Intent Classifier for Supporting High School Students K. Assayed, Suha |
| title | A Chatbot Intent Classifier for Supporting High School Students |
| title_full | A Chatbot Intent Classifier for Supporting High School Students |
| title_fullStr | A Chatbot Intent Classifier for Supporting High School Students |
| title_full_unstemmed | A Chatbot Intent Classifier for Supporting High School Students |
| title_short | A Chatbot Intent Classifier for Supporting High School Students |
| title_sort | A Chatbot Intent Classifier for Supporting High School Students |
| url | https://bspace.buid.ac.ae/handle/1234/3009 |