Text Mining Approaches Oriented on Customer Care Efficiency

Massaro, Alessandro and Magaletti, Nicola and Cosoli, Gabriele and Giardinelli, Vito and Leogrande, Angelo (2022): Text Mining Approaches Oriented on Customer Care Efficiency.

Abstract

In the proposed work is performed a text classification for a chatbot application used by a company working in assistance services of automatic warehouses. industries. Specifically, text mining technique is adopted for the classification of questions and answers. Business Process Modeling Notation (BPMN) models describe the passage from “AS-IS” to “TO BE” in the context of the analyzed industry, by focusing the attention mainly on customer and technical support services where chatbot is adopted. A two-step process model is used to connect technological improvements and relationship marketing in chatbot assistance: the first step provides the hierarchical clustering able to classify questions and answers through Latent Dirichlet Allocation -LDA- algorithm, and the second one executes the Tag Cloud representing the visual representation of more frequent words contained in the experimental dataset. Tag cloud is used to show the critical issues that customers find in the usage of the proposed service. By considering an initial dataset, 24 hierarchical clusters are found representing the preliminary combination of the couple’s question-answer. The proposed approach is suitable to automatically construct a combination of chatbot questions and appropriate answers in intelligent systems.

• Item Type: MPRA Paper
• Language: English
• Keywords: Chatbot, Speech Recognition, Natural Language Processing-NLP, Hierarchical Clustering, Business Process Management and Notation-BPMN.
• Subjects: O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O30 - General; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O32 - Management of Technological Innovation and R&D; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O34 - Intellectual Property and Intellectual Capital

References

Al Islami, M. T. F., Barakbah, A. R. & Harsono, T., 2020. Interactive Applied Graph Chatbot with Semantic Recognition. 2020 International Electronics Symposium (IES), pp. 557-564.

Ashok, M., Ramasamy, K., Snehitha, G. & Keerthi, S. R., 2021 . A Systematic Survey of Cognitive Chatbots in Personalized Learning Framework. 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 241-245.

Behera, R. K., Bala, P. K. & Ray, A., 2021. Cognitive Chatbot for personalised contextual customer service: Behind the scene and beyond the hype. Information Systems Frontiers, pp. 1-21.

Chao, M. H., Trappey, A. J. & Wu, C. T., 2021. Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics Complexity.

Chou, Y. C., Chao, C. Y. & Yu, H. Y., 2019. A Résumé Evaluation System Based on Text Mining. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 052-057.

Chung, M., Ko, E., Joung, H. & Kim, S. J., 2020. Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, Volume 117, pp. 587-595.

Dharani, M. et al., 2020. Interactive Transport Enquiry with AI Chatbot. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 1(IEEE), pp. 1271-1276.

Dharaniya, R., Vijayalakshmi, K., Tejasree, R. & Naveena, P., 2020. Survey on interactive chatbot. International Journal for Research in Applied Science & Engineering Technology (IJRASET) , 8(IV).

Dodero, J. R., Flaviani, F. & Alarcón, S., 2020. Analysis on Formality Classification in Conversational Bots. Octava Conferencia Nacional de Computación, Informática y Sistemas / CoNCISa, pp. 67-71. Electronic copy available at: https://ssrn.com/abstract=4026102

Følstad, A. & Taylor, C., 2021. Investigating the user experience of customer service chatbot interaction: a framework for qualitative analysis of chatbot dialogues. Quality and User Experience, 6(1), pp. 1-17.

Hien, H. T. et al., 2018. Intelligent assistants in higher-education environments: the FIT-EBot, a chatbot for administrative and learning support. Proceedings of the ninth international symposium on information and communication technology, pp. 69-76.

Ikemoto, Y., Asawavetvutt, V., Kuwabara, K. & Huang, H. H., 2018. Conversation strategy of a chatbot for interactive recommendations. Asian Conference on Intelligent Information and Database Systems, Springer(Cham), pp. 117-126.

Kowatsch, T. et al., 2017. Text-based healthcare chatbots supporting patient and health professional teams: preliminary results of a randomized controlled trial on child.

Kushwaha, A. K., Kumar, P. & Kar, A. K., 2021. What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, Volume 98, pp. 207-221.

Landim, A. R. D. B. et al., 2021. Chatbot design approaches for fashion E-commerce: an interdisciplinary review. International Journal of Fashion Design, Technology and Education .

Lommatzsch, A., 2018. A next generation chatbot-framework for the public administration. International Conference on Innovations for Community Services, pp. 127-141.

Luo, B., Lau, R. Y., Li, C. & Si, Y. W., 2021. A critical review of state-of-the-art chatbot designs and applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. 1434.

Madhu, D. et al., 2017. A novel approach for medical assistance using trained chatbot. 2017 international conference on inventive communication and computational technologies (ICICCT), pp. 243-246.

Maeda, H., Saiki, S., Nakamura, M. & Yasuda, K., 2019. Recording Daily Health Status with Chatbot on Mobile Phone-A Preliminary Study. 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU), pp. 1-6.

Mantravadi, S., Jansson, A. D. & Møller, C., 2020. User-Friendly MES Interfaces: Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors. 12th Asian Conference on Intelligent Information and Database Systems.

Massaro, A., 2021. Electronic in Advanced Research Industry: From Industry 4.0 to Industry 5.0 Advances. Wiley/IEEE, p. ISBN.

Massaro, A. & Galiano, A., 2020. Re-engineering process in a food factory: an overview of technologies and approaches for the design of pasta production processes. Production & Manufacturing Research, 8(1), pp. 80-100.

Massaro, A., Gargaro, M., Birardi, G. & Galiano, A., 2021. Combined CNN and LSTM Models for Robotic Financial Guidance. Informatica, 4(32), pp. 2-20, Electronic copy available at: https://ssrn.com/abstract=4026102

Massaro, A., Maritati, V. & Galiano, A., 2018. Automated Self-learning Chatbot Initially Build as a FAQs Database Information Retrieval System: Multi-level and Intelligent Universal Virtual Frontoffice Implementing Neural Network. Informatica, 42(4).

Massaro, A., Meuli, G., Savino.N. & Galiano, A., 2020. Voice Analysis Rehabilitation Platform based on LSTM Algorithm. International Journal of Telemedicine and Clinical Practices , Volume 1.

Massaro, A., Vitti, V., Galiano, A. & Morelli, A., 2019. Business intelligence improved by data mining algorithms and big data systems: an overview of different tools applied in industrial research. Computer Science and Information Technology, 7(1), pp. 1-21.

Mathew, R. B., Varghese, S., Joy, S. E. & Alex, S. S., 2019. Chatbot for disease prediction and treatment recommendation using machine learning. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) , pp. 851-856.

Murad, D. F. et al., 2019. Towards smart LMS to improve learning outcomes students using LenoBot with natural language processing. 2019 6th International Conference on Information Technology Computer and Electrical Engineering (ICITACEE), pp. 1-6.

Nagarhalli, T. P., Vaze, V. & Rana, N. K., 2020. A Review of Current Trends in the Development of Chatbot Systems. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Volume IEEE, pp. 706-710.

Omoregbe, N. A. et al., 2020. Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic. Journal of Healthcare Engineering.

Pantano, E. & Pizzi, G., 2020. Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, Issue 102096 p. 55.

Paul, A., Haque Latif, A., Amin Adnan, F. & Rahman, R. M., 2019. Focused domain contextual AI chatbot framework for resource poor languages. Journal of Information and Telecommunication, 3(2), pp. 248-269.

Pizzi, G., Scarpi, D. & Pantano, E., 2021. Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot?. Journal of Business Research, Volume 129, pp. 878-890.

Prasad, V. A. & Ranjith, R., 2020. Intelligent Chatbot for Lab Security and Automation. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Volume IEEE, pp. 1-4.

Przegalinska, A. et al., 2019. In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6), pp. 785-797.

Qaffas, A. A., 2019. Improvement of Chatbots semantics using wit. ai and word sequence kernel: Education Chatbot as a case study. International Journal of Modern Education and Computer Science, 11(16).

Sari, A. C. et al., 2020. Chatbot Developments in The Business World. Advances in Science, Technology and Engineering Systems Journal, 5(6), pp. 627-635. Electronic copy available at: https://ssrn.com/abstract=4026102

Setiawan, B. M., Zulhawati, Z., Maitri, A. L. & Sutopo, J., 2019. Chatbot Services at Educational Institutions with Customer Relationship Management. Journal of International Conference Proceedings (JICP), 2(1), p. 17.

Sheth, A., Yip, H. Y. & Shekarpour, S., 2019. Extending patient-chatbot experience with internet-ofthings and background knowledge: case studies with healthcare applications. IEEE Intelligent Systems, 4(34), pp. 24-30.

Siangchin, N. & Samanchuen, T., 2019. Chatbot implementation for ICD-10 recommendation system. 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI, Volume IEEE, pp. 1-6.

Sidaoui, K., Jaakkola, M. & Burton, J., 2020. AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management.

Tjiptomongsoguno, A. R. W. et al., 2020. Medical Chatbot Techniques: A Review. Proceedings of the Computational Methods in Systems and Software, Springer(Cham), pp. 346-356.

Yorita, A., Egerton, S., Chan, C. & Kubota, N., 2020. Chatbot for Peer Support Realization based on Mutual Care. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Volume IEEE, pp. 1601-1606