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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/3011 https://doi.org/10.4108/eetsis.v10i2.2948. |
| الوسوم: |
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| _version_ | 1862980615669809152 |
|---|---|
| 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 | 2023-04-01 2025-05-14T09:19:53Z 2025-05-14T09:19:53Z |
| dc.identifier.none.fl_str_mv | 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. 2032-9407 https://bspace.buid.ac.ae/handle/1234/3011 https://doi.org/10.4108/eetsis.v10i2.2948. |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | EAI Endorsed Transactions on Scalable Information Systems |
| dc.relation.none.fl_str_mv | ICST Transactions on Scalable Information Systems(20221221): e1 |
| dc.subject.none.fl_str_mv | intent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp |
| 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_ca625f8ae6dfd6543cdadd13adb833e3 |
| identifier_str_mv | 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. 2032-9407 |
| language_invalid_str_mv | en_US |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3011 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | EAI Endorsed Transactions on Scalable Information Systems |
| 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, Manarintent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlpINTRODUCTION: 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.EAI Endorsed Transactions on Scalable Information Systems2025-05-14T09:19:53Z2025-05-14T09:19:53Z2023-04-01ArticleAssayed, S.K., Shaalan, K. and Alkhatib, M. (2022) “A Chatbot Intent Classifier for Supporting High School Students,” ICST Transactions on Scalable Information Systems, p. e1.2032-9407https://bspace.buid.ac.ae/handle/1234/3011https://doi.org/10.4108/eetsis.v10i2.2948.en_USICST Transactions on Scalable Information Systems(20221221): e1oai:bspace.buid.ac.ae:1234/30112025-08-12T17:24:49Z |
| spellingShingle | A Chatbot Intent Classifier for Supporting High School Students K. Assayed, Suha intent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp |
| 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 |
| topic | intent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp |
| url | https://bspace.buid.ac.ae/handle/1234/3011 https://doi.org/10.4108/eetsis.v10i2.2948. |