Использование онтологии для анализа английских комментариев в социальных сетях
Ключевые слова:
чат-бот, онтология, глубокое обучение, машинное обучение, социальная сеть, ВьетнамАннотация
Чат-боты заинтересовывают многих пользователей по мере того, как технологии становятся все более продвинутыми. Потребность в обмене информацией между людьми через компьютерные системы увеличивается с каждым днем, в результате чего в большинстве стран растет предпочтение использовать чат-боты. Поскольку Вьетнам является развивающейся страной с множеством этнических групп, требуется усиленное внимание к распространению социальных сетей и расширению кооперативной экономики. Серьезной проблемой стало неуместное использование слов в повседневной жизни. В социальных сетях встречаются неоднозначные отзывы с похвалой и критикой о том, что мы пытаемся уменьшить использование негативной лексики и улучшить качество использования языка в социальных сетях. Мы стремимся удовлетворить потребности пользователей в социальных сетях, способствовать экономическому развитию и более эффективно решать социальные проблемы. Для достижения этих целей предлагается метод глубокого обучения, использующий интеллектуальный анализ онтологических знаний для сбора и обработки комментариев в социальных сетях. Этот подход направлен на улучшение пользовательского опыта и облегчение обмена информацией между людьми путем анализа мнений в комментариях. Результаты экспериментов показывают, что наш метод превосходит традиционный подход.
Литература
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2. Rehmat U., Javed A., Nasir M., Bashir M. Sentimental analysis of beauty brands on social media. Proc. GS Int. Conf. on Computer Science on Engineering 2020 (GSICCSE 2020). 2020. no. 37.
3. Chen Y., Wang L. Misleading political advertising fuels incivility online: A social network analysis of 2020 us presidential election campaign video comments on YouTube. Computers in Human Behavior. 2022. vol. 131(37). DOI: 10.1016/j.chb.2022.107202.
4. Nguyen H., Tan N., Quan N., Huong T., Phat H. Building a chat-bot system to analyze opinions of english comments. Informatics and Automation. 2023. vol. 22. no. 2. pp. 289–315.
5. Fidan M., Gencel N. Supporting the instructional videos with chat-bot and peer feedback mechanisms in online learning: The effects on learning performance and intrinsic motivation. Journal of Educational Computing Research. 2022. vol. 60. no. 7. pp. 1716–1741.
6. Wu E., Lin C.-H., Ou Y.-Y., Liu C.-Z., Wang W.-K., Chao C.-Y. Advantages and constraints of a hybrid model k-12 e-learning assistant chatbot. Ieee Access. 2020. vol. 8. pp. 77788–77801.
7. Surani D., Hamidah H. Students perceptions in online class learning during the covid-19 pandemic. International Journal on Advanced Science, Education, and Religion. 2020. vol. 3. no. 3. pp. 83–95.
8. Suta P., Lan X., Wu B., Mongkolnam P., Chan J. An overview of machine learning in chatbots. International Journal of Mechanical Engineering and Robotics Research. 2020. vol. 9. no. 4. pp. 502–510.
9. Adamopoulou E., Moussiades L. An overview of chatbot technology. IFIP international conference on artificial intelligence applications and innovations. Springer, 2020. pp. 373–383.
10. Nguyen T., Ho D., Nguyen N. An ontology-based question answering system for university admissions advising. Intelligent Automation & Soft Computing. 2023. vol. 36. no. 1. pp. 601–616. DOI: 10.32604/iasc.2023.032080.
11. Zahour O., Eddaoui A., Ouchra H., Hourrane O., Benlahmar E. A system for educational and vocational guidance in morocco: Chatbot e-orientation. Procedia Computer Science. 2020. vol. 175. pp. 554–559. DOI: j.procs.2020.07.079.
12. Hallili A. Toward an ontology-based chatbot endowed with natural language processing and generation. Processing of the 26th european summer school in logic, language & information. 2014. 7 p.
13. Avila C., Calixto A., Rolim T., Franco W., Venceslau A., Vidal V., Pequeno V., Moura F. Medibot: an ontology-based chatbot for Portuguese speakers drug’s users. International Conference on Enterprise Information Systems. 2019. vol. 1. pp. 25–36.
14. Muangkammuen P., Intiruk N., Saikaew K. Automated thai-faq chatbot using rnn-lstm. in 2018 22nd International Computer Science and Engineering Conference (ICSEC). IEEE, 2018. pp. 1–4.
15. Blanc C., Bailly A., Francis E., Guillotin T., Jamal F., Wakim B., Roy P. Flaubert vs. camembert: Understanding patient’s answers by a french medical chatbot. Artificial Intelligence in Medicine. 2022. vol. 127.
16. Hung N., Loi T., Binh N., Nga N., Huong T., Luu D. Building an online learning model through a dance recognition video based on deep learning. Informatics and Automation. 2024. vol. 23. no. 1. pp. 101–128.
17. Dhyani M., Kumar R. An intelligent chatbot using deep learning with bidirectional rnn and attention model. Materials today: proceedings. 2021. vol. 34. pp. 817–824.
18. Hung N., Loi T., Huong N., Hang T., Huong T. Aafndl-an accurate fake information recognition model using deep learning for the vietnamese language. Informatics and Automation. 2023. vol. 22. no. 4. pp. 795–825.
19. Suganthi S., Ayoobkhan M., Kumar K., Bacanin N., Venkatachalam K., Hubalovsky S., Trojovsky P. Deep learning model for deep fake face recognition and detection. PeerJ Computer Science. 2022. vol. 8. DOI: 10.7717/peerj-cs.881.
20. Rakib A., Rumky E., Ashraf A., Hillas M., Rahman M. Mental healthcare chatbot using sequence-to-sequence learning and bilstm. International Conference on Brain Informatics. Springer, 2021. pp. 378–387.
21. Palasundram K., Sharef N., Nasharuddin N., Kasmiran K., Azman A. Sequence to sequence model performance for education chatbot. International journal of emerging Technologies in Learning (iJET). 2019. vol. 14. no. 24. pp. 56–68.
22. Piccini R., Spanakis G. Exploring the context of recurrent neural network based conversational agents. arXiv preprint arXiv:1901.11462. 2019.
23. Sun R., Chen B., Zhou Q., Li Y., Cao Y., Zheng H.-T. A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. pp. 6582–6586.
24. Karna M., Juliet D., Joy R. Deep learning-based text emotion recognition for chatbot applications. 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184). IEEE, 2020. pp. 988–993.
25. Sperli G. A cultural heritage framework using a deep learning-based chatbot for supporting tourist journey. Expert Systems with Applications. 2021. vol. 183. DOI: 10.1016/j.eswa.2021.115277.
26. Hitzler P., Krotzsch M., Parsia B., Patel-Schneider P., Rudolph S., et al. Owl 2 web ontology language primer. W3C recommendation. 2009. vol. 27. no. 1.
27. Antoniou G., Groth P., Van Harmelen F., Hoekstra R. A semantic web primer 3rd ed. The MIT Press, 2012. 288 p.
28. Casillo M., De Santo M., Mosca R., Santaniello D. An ontology-based chatbot to enhance experiential learning in a cultural heritage scenario. Frontiers in Artificial Intelligence. 2022. vol. 5.
29. Chi Y.-L., Sung H.-Y. Building the knowledge base of folk beliefs based on semantic web technology. International Conference on Human-Computer Interaction. Springer Nature Switzerland, 2023. pp. 172–182.
30. Owl web ontology language. Available: https://www.w3.org/TR/owl-features/ (accessed: 2023-08-15).
31. Aceta C., Fernandez I., Soroa A. Todo: A core ontology for task-oriented dialogue systems in industry 4.0. Further with Knowledge Graphs. IOS Press, 2021. pp. 1–15.
32. Bouziane A., Bouchiha D., Doumi N., Malki M. Question answering systems: survey and trends. Procedia Computer Science. 2015. vol. 73. pp. 366–375.
33. Budler L.C., Gosak L., Stiglic G. Review of artificial intelligence-based question-answering systems in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2023. vol. 13. no. 2.
34. Majid M., Hayat M., Khan F., Ahmad M., Jhanjhi N., Bhuiyan M., Masud M., AlZain M. Ontology-based system for educational program counseling. Intelligent Automation and Soft Computing. 2021. vol. 30. no. 1. pp. 373–386.
35. Kasthuri E., Balaji S. A chatbot for changing lifestyle in education. Proceedings of the Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2021. pp. 1317–1322.
36. Dharani M., Jyostna J., Sucharitha E., Likitha R., Manne S. Interactive transport enquiry with ai chatbot. Proceedings of the 4th International Conference on Intelligent Computing and Control Systems (ICICCS). 2020. pp. 1271–1276.
37. Nguyen H., Dao T., Pham N., Dang T., Nguyen T., Truong T. An accurate viewport estimation method for 360 video streaming using deep learning. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. 2022. vol. 9. no. 4.
Опубликован
2024-09-25
Как цитировать
Вьет Хунг, Н., Тан, Н., Тхи Туй Нга, Н., Хуен Транг, Л. Т., & Туй Ханг, Т. Т. (2024). Использование онтологии для анализа английских комментариев в социальных сетях. Информатика и автоматизация, 23(5), 1311-1338. https://doi.org/10.15622/ia.23.5.2
Раздел
Искусственный интеллект, инженерия данных и знаний
Copyright (c) Hung Viet Nguyen
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