Authors :
Archit Gupta; Dr. Tanya Singh
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/37y4shup
Scribd :
https://tinyurl.com/2f3ssrt3
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1290
Abstract :
Chatbots are becoming very useful in almost
every sector of our daily and even corporate life.
Working with chatbots gives us a personalised feeling in
whatever we are doing. This has created a need for
creating chatbots for software related issues. Developing
a chatbot is not easy as we have to work on many things
simultaneously and maintain everything, therefore
selecting a platform or framework to develop an
intelligent chatbot has become a crucial step.
This study presents a comprehensive analysis of
various frameworks utilised in the development of
intelligent chatbots. Through a thorough examination of
platforms and frameworks, the research aims to provide
insights into their functionalities, architectures, features,
and performance metrics. Comparative assessments are
conducted to evaluate the strengths, weaknesses, and
performance characteristics of selected frameworks. The
findings reveal that Microsoft bot framework offers
simplicity and almost every feature required to build the
chatbot efficiently.
Keywords :
Chatbot, Dialog Flow, IBM Watson, RASA, Microsoft Bot, Botkit, NLP.
References :
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- Vidya G, Nagaraj Krishna Vernekar, Ganraj Kelkar (June 2023). Comparative-study-on-chatbot-frameworks https://www.ijraset.com/research-paper/comparative-study-on-chatbot-framework
- Md Imran Pavel. (May 2021). COMPARING CHATBOT FRAMEWORKS: A STUDY OF RASA AND BOTKIT https://trepo.tuni.fi/handle/10024/132928
- Ioannis Dagkoulis, Lefteris Moussiades.(November 2022). A Comparative Evaluation of Chatbot Development Platforms https://dl.acm.org/doi/abs/10.1145/3575879.3576012
- Daniel Braun, Florian Matthes. (2019). Towards a Framework for Classifying Chatbots https://www.researchgate.net/publication/332902947_Towards_a_Framework_for_Classifying_Chatbots
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multi task learners https://blocksml.com/genaipapers/Language%20Models%20are%20Unsupervised%20Multitask%20Lea rners.pdf
- Nachiket Kapure. (September 2022). COMPARATIVE ANALYSIS OF VARIOUS CHATBOT FRAMEWORKS https://www.irjmets.com/uploadedfiles/paper//issue_9_september_2022/29979/final/fin_irjmets1663344794.pdf
- Aarsh Trivedi, Vatsal Gor ,Zalak Thakkar. (2019). Chatbot generation and integration: A review https://www.ijariit.com/manuscripts/v5i2/V5I2-1840.pdf
- Antje Janssen ,Davinia Rodríguez Cardona , Jens Passlick, Michael H. Breitner. (2022). How to Make chatbots productive – A user-oriented implementation framework https://www.sciencedirect.com/science/article/pii/S1071581922001410
- Rakesh Kumar Sharma, Manoj Joshi. (June 2020). An Analytical Study and Review of open Source Chatbot framework, RASA https://www.researchgate.net/publication/342537790_An_Analytical_Study_and_Review_of_open_source_Chatbot_framework_Rasa
- GWENDAL DANIEL, JORDI CABOT. (2019). Xatkit: A Multimodal Low-Code Chatbot Development Framework https://www.researchgate.net/publication/338616011_Xatkit_A_Multimodal_Low-Code_Chatbot_Development_Framework
- Martin Adam & Michael Wessel & Alexander Benlian. (2020). AI-based chatbots in customer service and their effects on user compliance https://link.springer.com/article/10.1007/s12525-020-00414-7
- Guendalina Caldarini , Sardar Jaf † and Kenneth McGarry. (2022). A Literature Survey of Recent Advances in Chatbots https://www.mdpi.com/2078-2489/13/1/41
- Savvas Varitimidis, Konstantinos Kotis, Dimitra Pittou and Georgios Konstantakis. (2021). Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums https://www.mdpi.com/2076-3417/11/19/9160
- Daniel Adiwardana Minh-Thang Luong .(2020). Towards a Human-like Open-Domain Chatbot https://arxiv.org/abs/2001.09977
- Tejas Pillare, Manisha Chaoudhari. (2023). A SURVEY PAPER ON CHATBOT https://www.irjmets.com/uploadedfiles/paper//issue_10_october_2023/45644/final/fin_irjmets1698841142.pdf
- Khe Foon Hew, Weijiao Huang. (2022). Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions https://link.springer.com/article/10.1007/s12528-022-09338-x
- Yurio Windiatmoko et al. (2021). Developing Facebook Chatbot Based on Deep Learning Using RASA Framework for University Enquiries https://iopscience.iop.org/article/10.1088/1757-899X/1077/1/012060/pd
Chatbots are becoming very useful in almost
every sector of our daily and even corporate life.
Working with chatbots gives us a personalised feeling in
whatever we are doing. This has created a need for
creating chatbots for software related issues. Developing
a chatbot is not easy as we have to work on many things
simultaneously and maintain everything, therefore
selecting a platform or framework to develop an
intelligent chatbot has become a crucial step.
This study presents a comprehensive analysis of
various frameworks utilised in the development of
intelligent chatbots. Through a thorough examination of
platforms and frameworks, the research aims to provide
insights into their functionalities, architectures, features,
and performance metrics. Comparative assessments are
conducted to evaluate the strengths, weaknesses, and
performance characteristics of selected frameworks. The
findings reveal that Microsoft bot framework offers
simplicity and almost every feature required to build the
chatbot efficiently.
Keywords :
Chatbot, Dialog Flow, IBM Watson, RASA, Microsoft Bot, Botkit, NLP.
