Abstract
Chatbot yani sohbet robotu; kullanıcıların bilgisayar ile sesli veya yazılı olarak iletişime geçtiği bir uygulamadır. Günümüzde chatbotlar yaygın olarak birçok sektörde kullanılmaktadır. Chatbotlar kural tabanlı ve makine öğrenme temelli olmak üzere temel olarak iki şekilde tasarlanmaktadır. Bu çalışmada gerçek bir işletmenin çağrı merkezi işlemlerini yönetebilmek adına makina öğrenimi ile çeşitli doğal dil işleme (NLP) teknikleri kullanılarak bir chat bot tasarımı gerçekleştirilmiştir. Bu sohbet robotunun geliştirilmesindeki temel amaç kullanıcıların firma veya firma ürünleri hakkındaki soru veya soruları karşısında bir çalışana ihtiyaç duymadan hızlı ve efektif bir şekilde çözüm bulmasıdır. Bu sohbet robotunda kullanıcı sorular sorarak girdiler oluşturmaktadır. Bu girdilere yanıt olarak ise veri setinde uygun alan altındaki oluşturulmuş responses alanı cevap olarak kullanıcıya dönmektedir. Veri setinde ise niyet, bu niyete ait olan patern ve verilmesi gereken cevaplar bulunan bir json dosyası kullanılmaktadır. Veri setini oluşturan patern ve cevaplar firmanın sıkça sorulan sorular (S.S.S) bölümüyle birlikte, firmanın çağrı merkezine gelen telefon konuşmaları ve whatsApp müşteri hizmetleri hattındaki veriler ile oluşturulmuştur. Eğitilen model 32 gün boyunca her gün belli saat aralıklarında canlı olarak devreye alınmış ve chatbotun sorulan sorulara karşın verdiği cevapların oldukça yüksek olduğu görülmüştür.
Keywords
References
- A. M. Turing COMPUTING MACHINERY AND INTELLIGENCE https://academic.oup.com/mind/article/LIX/236/433/986238
- Alex Debecker2020 Chatbot Statistics - All The Data You Need https://blog.ubisend.com/optimise-chatbots/chatbot-statistics
- Mobile Web Predictions for 2020- https://deviceatlas.com/blog/15-mobile-web-predictions-2020
- Ming Hsiang Su, Chung Hsien Wu, Kun Yi Huang, Qian Bei Hong, Hsin Min Wang- A chatbot using LSTM-based multi-layer embedding for elderly care- https://researchoutput.ncku.edu.tw/en/publications/a-chatbot-using-lstm-based-multi-layer-embedding-for-elderly-care
- Suzana Ilic´,Reiichiro Nakano,Ivo Hajnal-Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser- https://arxiv.org/pdf/1909.09531.pdf
- Victoria Oguntosin Development of an E-Commerce Chatbot for a University-Shopping-Mall- https://www.hindawi.com/journals/acisc/2021/6630326/#introduction
- Ali Hakan ISIK,Ayşenur YAĞCI Sequence to Sequence LSTM Modeli ile Telegram-Bot-Uygulaması- https://dergipark.org.tr/en/pub/gmbd/issue/54119/693071
- A. C. Sari, N. Virnilia, J. T. Susanto, K. A. Phiedono, and T. K. Hartono, “Chatbot developments in the business world,” Adv. Sci. Technol. Eng. Syst., 2020, doi: 10.25046/aj050676.
- S. Roca, J. Sancho, J. García, and Á. Alesanco, “Microservice chatbot architecture for chronic patient support,” J. Biomed. Inform., 2020, doi: 10.1016/j.jbi.2019.103305.
- S. Hwang and J. Kim, “Toward a chatbot for financial sustainability,” Sustain., 2021, doi: 10.3390/su13063173.
- M. Dahiya, “A Tool of Conversation: Chatbot,” Int. J. Comput. Sci. Engenieering, 2017.
- B. A. Shawar and E. Atwell, “ALICE chatbot: Trials and outputs,” Comput. y Sist., 2015, doi: 10.13053/CyS-19-4-2326.
- C. Grové, “Co-developing a Mental Health and Wellbeing Chatbot With and for Young People,” Front. Psychiatry, 2021, doi: 10.3389/fpsyt.2020.606041.
- G. Padmaja, M. S. Begum, A. Chandrika, B. B. Sree, and G. Meghana, “Healthcare Chatbot,” UGC Care List. J., 2020.
- S. Hamad and T. Yeferny, “A chatbot for information security,” arXiv, 2020.
- P. A. Tamayo, A. Herrero, J. Martín, C. Navarro, and J. M. Tránchez, “Design of a chatbot as a distance learning assistant,” Open Prax., 2020, doi: 10.5944/openpraxis.12.1.1063.
- M. H. Tsai, J. Y. Chen, and S. C. Kang, “Ask Diana: A keyword-based chatbot system for water-related disaster management,” Water (Switzerland), 2019, doi: 10.3390/w11020234.
- D. C. Toader et al., “The effect of social presence and chatbot errors on trust,” Sustain., 2020, doi: 10.3390/SU12010256.
- Q. Zhi and R. Metoyer, “GameBot: A visualization-augmented chatbot for sports game,” 2020, doi: 10.1145/3334480.3382794.
- I. Nica, O. A. Tazl, and F. Wotawa, “Chatbot-based tourist recommendations using model-based reasoning,” 2018.
- M. H. Tsai, J. Y. Chen, and S. C. Kang, “Ask Diana: A keyword-based chatbot system for water-related disaster management,” Water (Switzerland), 2019, doi: 10.3390/w11020234.
- P. A. Tamayo, A. Herrero, J. Martín, C. Navarro, and J. M. Tránchez, “Design of a chatbot as a distance learning assistant,” Open Prax., 2020, doi: 10.5944/openpraxis.12.1.1063.
Abstract
It is an application where users communicate with the computer by voice or in writing. Today, chatbots are widely used in many industries. Chatbots are basically designed in two ways: rule-based and machine learning-based. In this study, a chat bot design was carried out using machine learning and various natural language processing (NLP) techniques in order to manage the call center operations of a real business. The main purpose of the development of this chat robot is to find solutions quickly and effectively without the need of an employee in the face of questions or questions about the company or company products. In this chatbot, the user creates inputs by asking questions. In response to these inputs, the responses field created under the appropriate field in the data set returns to the user in response. In the dataset, a json file is used with the intent, the pattern belonging to this intent and the answers to be given. The pattern and answers that make up the data set were created with the frequently asked questions (FAQ) section of the company, phone calls from the company's call center and data from the whatsApp customer service line. The trained model was activated live at certain hours every day for 32 days and it was seen that the answers given by the chatbot were quite high despite the questions asked..
Keywords
References
- A. M. Turing COMPUTING MACHINERY AND INTELLIGENCE https://academic.oup.com/mind/article/LIX/236/433/986238
- Alex Debecker2020 Chatbot Statistics - All The Data You Need https://blog.ubisend.com/optimise-chatbots/chatbot-statistics
- Mobile Web Predictions for 2020- https://deviceatlas.com/blog/15-mobile-web-predictions-2020
- Ming Hsiang Su, Chung Hsien Wu, Kun Yi Huang, Qian Bei Hong, Hsin Min Wang- A chatbot using LSTM-based multi-layer embedding for elderly care- https://researchoutput.ncku.edu.tw/en/publications/a-chatbot-using-lstm-based-multi-layer-embedding-for-elderly-care
- Suzana Ilic´,Reiichiro Nakano,Ivo Hajnal-Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser- https://arxiv.org/pdf/1909.09531.pdf
- Victoria Oguntosin Development of an E-Commerce Chatbot for a University-Shopping-Mall- https://www.hindawi.com/journals/acisc/2021/6630326/#introduction
- Ali Hakan ISIK,Ayşenur YAĞCI Sequence to Sequence LSTM Modeli ile Telegram-Bot-Uygulaması- https://dergipark.org.tr/en/pub/gmbd/issue/54119/693071
- A. C. Sari, N. Virnilia, J. T. Susanto, K. A. Phiedono, and T. K. Hartono, “Chatbot developments in the business world,” Adv. Sci. Technol. Eng. Syst., 2020, doi: 10.25046/aj050676.
- S. Roca, J. Sancho, J. García, and Á. Alesanco, “Microservice chatbot architecture for chronic patient support,” J. Biomed. Inform., 2020, doi: 10.1016/j.jbi.2019.103305.
- S. Hwang and J. Kim, “Toward a chatbot for financial sustainability,” Sustain., 2021, doi: 10.3390/su13063173.
- M. Dahiya, “A Tool of Conversation: Chatbot,” Int. J. Comput. Sci. Engenieering, 2017.
- B. A. Shawar and E. Atwell, “ALICE chatbot: Trials and outputs,” Comput. y Sist., 2015, doi: 10.13053/CyS-19-4-2326.
- C. Grové, “Co-developing a Mental Health and Wellbeing Chatbot With and for Young People,” Front. Psychiatry, 2021, doi: 10.3389/fpsyt.2020.606041.
- G. Padmaja, M. S. Begum, A. Chandrika, B. B. Sree, and G. Meghana, “Healthcare Chatbot,” UGC Care List. J., 2020.
- S. Hamad and T. Yeferny, “A chatbot for information security,” arXiv, 2020.
- P. A. Tamayo, A. Herrero, J. Martín, C. Navarro, and J. M. Tránchez, “Design of a chatbot as a distance learning assistant,” Open Prax., 2020, doi: 10.5944/openpraxis.12.1.1063.
- M. H. Tsai, J. Y. Chen, and S. C. Kang, “Ask Diana: A keyword-based chatbot system for water-related disaster management,” Water (Switzerland), 2019, doi: 10.3390/w11020234.
- D. C. Toader et al., “The effect of social presence and chatbot errors on trust,” Sustain., 2020, doi: 10.3390/SU12010256.
- Q. Zhi and R. Metoyer, “GameBot: A visualization-augmented chatbot for sports game,” 2020, doi: 10.1145/3334480.3382794.
- I. Nica, O. A. Tazl, and F. Wotawa, “Chatbot-based tourist recommendations using model-based reasoning,” 2018.
- M. H. Tsai, J. Y. Chen, and S. C. Kang, “Ask Diana: A keyword-based chatbot system for water-related disaster management,” Water (Switzerland), 2019, doi: 10.3390/w11020234.
- P. A. Tamayo, A. Herrero, J. Martín, C. Navarro, and J. M. Tránchez, “Design of a chatbot as a distance learning assistant,” Open Prax., 2020, doi: 10.5944/openpraxis.12.1.1063.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 15, 2021 |
| Publication Date | December 1, 2021 |
| Published in Issue | Year 2021 Issue: 29 |
