A Chatbot Intent Classifier for Supporting High School Students
Keywords:intent classification, features extraction, countvectorizer, tf-idf, multinomial naive-bayes, random forest, chatbot, nlp
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.
Santana, R., Ferreira, S., Rolim, V., de Miranda, P. B., Nascimento, A. C., & Mello, R. F. (2021). A Chatbot to Support Basic Students Questions. In LALA (pp. 58-67).
Zahour, O., El Habib Benlahmar, A. E., Ouchra, H., & Hourrane, O. (2020). Towards a Chatbot for educational and vocational guidance in Morocco: Chatbot E-Orientation. International Journal, 9(2).
Cranmore, J., Adams-Johnson, S. D., Wiley, J., & Holloway, A. (2019). Advising high school students for admission to college fine arts programs. Journal of School Counseling, (10).
Alonso, P. (2020). Faster and More Resource-Efficient Intent Classification (Doctoral dissertation, Luleå University of Technology).
Hefny, A. H., Dafoulas, G. A., & Ismail, M. A. (2020, December). Intent classification for a management conversational assistant. In 2020 15th International Conference on Computer Engineering and Systems (ICCES) (pp. 1-6). IEEE.
J. Schuurmans and F. Frasincar, "Intent Classification for Dialogue Utterances," in IEEE Intelligent Systems, vol. 35, no. 1, pp. 82-88, 1 Jan.-Feb. 2020, doi: 10.1109/MIS.2019.2954966.
Pérez-Vera, S., Alfaro, R., Allende-Cid, H. (2017). Intent Classification of Social Media Texts with Machine Learning for Customer Service Improvement. In: Meiselwitz, G. (eds) Social Computing and Social Media. Applications and Analytics. SCSM 2017. Lecture Notes in Computer Science(), vol 10283. Springer, Cham. https://doi.org/10.1007/978-3-319-58562-8_21
Hamad, S., & Yeferny, T. (2020). A chatbot for information security. arXiv preprint arXiv:2012.00826.
Shinde, N. V., Akhade, A., Bagad, P., Bhavsar, H., Wagh, S. K., & Kamble, A. (2021, June). Healthcare Chatbot System using Artificial Intelligence. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1-8). IEEE
Sarosa, M., Kusumawardani, M., Suyono, A., & Wijaya, M. H. (2020). Developing a social media-based Chatbot for English learning. In IOP Conference Series: Materials Science and Engineering (Vol. 732, No. 1, p. 012074). IOP Publishing.
Yang, H., Kim, H., Lee, J. H., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL, 1-17.
Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(1), 1-20.
Chen, H.-L., Vicki Widarso, G., & Sutrisno, H. (2020). A chatbot for learning chinese: learning achievement and technology acceptance. Journal of Educational Computing Research, 58(6), 1161–1189.
Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154-177.
Tseng, J.-J. (2018). Exploring tpack-sla interface: insights from the computer-enhanced classroom. Computer Assisted Language Learning, 31(4), 390–412.
Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279–289. https://doi.org/10.1016/j.chb.2018.12.023
American School Counselor Association (2022). School Counselor and Roles & Ratios. Retrieved from https://www.schoolcounselor.org/About-School-Counseling/School-Counselor-Roles-Ratios
Oripova, M. The Impact of Intrusive College Academic Advising on High School Students’ College Degree Attainment Commitment Levels: A Quantitative Quasi-Experimental Study. Available at SSRN 4076232.
Abbas, N., Whitfield, J., Atwell, E., Bowman, H., Pickard, T., & Walker, A. (2022). Online chat and chatbots to enhance mature student engagement in higher education. International Journal of Lifelong Education, 1-19.
Lin, A. P., Trappey, C. V., Luan, C. C., Trappey, A. J., & Tu, K. L. (2021). A Test Platform for Managing School Stress Using a Virtual Reality Group Chatbot Counseling System. Applied Sciences, 11(19), 9071
Kannan, S., Gurusamy, V., Vijayarani, S., Ilamathi, J., Nithya, M., Kannan, S., & Gurusamy, V. (2014). Preprocessing techniques for text mining. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
Q. Liu, J. Wang, D. Zhang, Y. Yang and N. Wang, "Text Features Extraction based on TF-IDF Associating Semantic," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 2338-2343, doi: 10.1109/CompComm.2018.8780663.
Zhao, G., Liu, Y., Zhang, W., & Wang, Y. (2018, January). TFIDF based feature words extraction and topic modeling for short text. In Proceedings of the 2018 2Nd International Conference on Management Engineering, Software Engineering and Service Sciences (pp. 188-191).
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2021). Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy. Pattern Recognition, 119, 108110.
IBM Cloud Education (2020, December,7). Random Forest https://www.ibm.com/cloud/learn/random-forest
Lemons, K., 2020. A Comparison Between Naïve Bayes and Random Forest to Predict Breast Cancer. International Journal of Undergraduate Research and Creative Activities, 12(1), pp.1–5. DOI: http://doi.org/10.7710/2168-0620.0287
How to Cite
Copyright (c) 2022 Suha Khalil Assayed, Khaled Shaalan, Manar Alkhatib
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.