Develop Institutional Chatbot Using Deep Neural Networks and NLTK
Keywords:
Deep Neural Networks, Natural Language, human-computer interaction, Artificial intelligence, Chatbots, Stochastic gradient descent (SGD), Natural Language Toolkit (NLTK)Abstract
Chatbots are intelligent software that can communicate and perform actions like those of a customer service representative. Chatbots are widely used for customer interaction and marketing on social networking and e-commerce sites. AI-based chatbots have the core ability to learn from any question based on initial training on a predefined dataset. A web-based platform provides a broad intelligent foundation for simulating human problem solving. The technology used here is based on deep neural networks and uses NLP for text processing and FLASK functionality for internal connectivity. Evolution has improved accuracy and performance rates on higher slopes. This recommended chatbot identifies the user context that triggers the specific intent of the response. Based on dynamic responses, it instantly generates the desired response for the user. The proposed system uses deep learning algorithms to train chatbots by experiencing different user responses and requests.
References
Shingte K, Chaudhari A, Patil A, Chaudhari A, Desai S. Chatbot Development for Educational Institute. Available at SSRN 3861241. 2021 Jun 6.
Vajinepalli Sai Harsha Vardhan, Parsi Anurag, Richa Sharma. Research Paper on Rule Based Chatbot. Int Res J Mod Eng Technol Sci. 2022; 4(5): 254–258.
Nofer M, Gomber P, Hinz O, Schiereck D. Blockchain. Bus Inf Syst Eng. 2017 Jun; 59(3): 183–7.
Chung M, Ko E, Joung H, Kim SJ. Chatbot e-service and customer satisfaction regarding luxury brands. J Bus Res. 2020 Sep 1; 117: 587–95.
Ashfaq M, Yun J, Yu S, Loureiro SM. I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telemat Inform. 2020 Nov 1; 54: 101473.
Prentice C, Dominique Lopes S, Wang X. The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. J Hosp Mark Manag. 2020 Oct 2; 29(7): 739–56.
Mahamad O, Ramayah T. Service quality, customer satisfaction and loyalty: A test of mediation. Int Bus Res. 2010 Oct 1; 3(4): 72–80.
Holzwarth M, Janiszewski C, Neumann MM. The influence of avatars on online consumer shopping behavior. J Mark. 2006 Oct; 70(4): 19–36.
Dabholkar PA, Thorpe DI, Rentz JO. A measure of service quality for retail stores: scale development and validation. J Acad Mark Sci. 1996 Dec; 24(1): 3–16.
Rapp A, Curti L, Boldi A. The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. Int J Hum-Comput Stud. 2021 Jul 1; 151: 102630.
Kasilingam DL. Understanding the attitude and intention to use smartphone chatbots for shopping. Technol Soc. 2020 Aug 1; 62: 101280.
Godey B, Manthiou A, Pederzoli D, Rokka J, Aiello G, Donvito R, Singh R. Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. J Bus Res. 2016 Dec 1; 69(12): 5833–41.
Kim C, Li W, Kim DJ. An empirical analysis of factors influencing M-shopping use. Int J Hum-Comput Interact. 2015 Dec 2; 31(12): 974–94.
Wang WT, Li HM. Factors influencing mobile services adoption: a brand‐equity perspective. Internet Res. 2012 Mar 30; 22(2): 142–79.
Nysveen H, Pedersen PE, Thorbjørnsen H. Intentions to use mobile services: Antecedents and cross-service comparisons. J Acad Mark Sci. 2005 Jul; 33(3): 330–46.
Adamopoulou E, Moussiades L. Chatbots: History, technology, and applications. Machine Learning with Applications (MLWA). 2020 Dec 15; 2: 100006.
Zumstein Darius, Hundertmark Sophie. Chatbots – An Interactive Technology for Personalized Communication, Transactions and Services. IADIS International Journal on WWW/Internet (IJWI). 2017; 15(1): 96–109.
Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 2019 Jan 1; 62(1): 15–25.
Xu Y, Shieh CH, van Esch P, Ling IL. AI customer service: Task complexity, problem-solving ability, and usage intention. Australas Mark J. 2020 Nov; 28(4): 189–99.
Zolkepli IA, Kamarulzaman Y. Social media adoption: The role of media needs and innovation characteristics. Comput Hum Behav. 2015 Feb 1; 43: 189–209.
Mangold WG, Faulds DJ. Social media: The new hybrid element of the promotion mix. Bus Horiz. 2009 Jul 1; 52(4): 357–65.
Muntinga DG, Moorman M, Smit EG. Introducing COBRAs: Exploring motivations for brand-related social media use. Int J Advert. 2011 Jan 1; 30(1): 13–46.
Cui L, Huang S, Wei F, Tan C, Duan C, Zhou M. Superagent: A customer service chatbot for e-commerce websites. In Proceedings of ACL 2017, system demonstrations. 2017 Jul; 97–102.
Köhler CF, Rohm AJ, De Ruyter K, Wetzels M. Return on interactivity: The impact of online agents on newcomer adjustment. J Mark. 2011 Mar; 75(2): 93–108.
Radziwill NM, Benton MC. Evaluating quality of chatbots and intelligent conversational agents. arXiv preprint arXiv:1704.04579. 2017 Apr 15.
Nadeem M, Arshad A, Riaz S, Zahra SW, Dutta AK, Almotairi S. Preventing the Cloud Networks through Semi-Supervised Clustering from Both Sides Attacks. Appl Sci. 2022 Jul 30; 12(15): 7701.
Arshad A, Nadeem M, Riaz S, Zahra SW, Dutta AK, Alzaid Z, Alabdan R, Almutairi B, Almotairi S. Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality. Comput Mater Contin. 2023 May 1; 75(2): 3065–3089.
