Electrical and Electronics Engineering: An International Journal (ELELIJ)

Electrical and Electronics Engineering:An International Journal (ELELIJ) Vol.12, No.2/3/4, November 2023
DOI: 10.14810/elelij.2023.12402 11
CREATION OF A CHATBOT BASED ON NATURAL
LANGUAGE PROCESSING FOR WHATSAPP
Valderrama Jonatan1
and Aguilar- Alonso Igor 1,2
1
San Marcos Lima, Peru
2
University of South Lima
ABSTRACT
In the era of digital transformation, customer service is of paramount importance to the success of
organizations, and to meet the growing demand for immediate responses and personalized assistance 24
hours a day, chatbots have become a promising tool to solve these problems. Currently, there are many
companies that need to provide these solutions to their customers, which motivates us to study this problem
and offer a suitable solution. The objective of this study is to develop a chatbot based on natural language
processing to improve customer satisfaction and improve the quality of service provided by the company
through WhatsApp. The solution focuses on creating a chatbot that efficiently and effectively handles user
queries. A literature review related to existing chatbots has been conducted, analyzing methodological
approaches, artificial intelligence techniques and quality attributes used in the implementation of chatbots.
The results found highlight that chatbots based on natural language processing enable fast and accurate
responses, which improves the efficiency of customer service, as chatbots contribute to customer
satisfaction by providing accurate answers and quick solutions to their queries at any time. Some authors
point out that artificial intelligence techniques, such as machine learning, improve the learning and
adaptability of chatbots as user interactions occur, so a good choice of appropriate natural language
understanding technologies is essential for optimal chatbot performance. The results of this study will
provide a solid foundation for the design and development of effective chatbots for customer service,
ensuring a satisfactory user experience and thus meeting the needs of the organization.
KEYWORDS
Natural Language Processing, Chatbot for WhatsApp, Chatbot development, Chatbot for Customer
Service.
1. INTRODUCTION
The customer service area plays a critical role in the success of any organization. With the
constant growth of e-commerce and the need for immediate feedback, it is important to provide
users with a satisfying and efficient experience. In this context, chatbots seem to be a promising
tool to provide automated and personalized support.This study focuses on developing a chatbot
based on natural language processing for WhatsApp, with the purpose of improving customer
satisfaction and service quality. The existing literature in the field of chatbots was reviewed in
detail, analysing methodological approaches, artificial intelligence techniques and quality
attributes used in the implementation of these systems. The literature has highlighted that
chatbots based on natural language processing allow fast and accurate responses, which translates
into a significant improvement in customer service efficiency [1]. Furthermore, chatbots have
been observed to contribute to customer satisfaction by providing accurate responses and quick
solutions to their queries [1]. Therefore, it is of vital importance to design a chatbot with a
Faculty of Systems Engineering and Informatics, National University of
Professional School of Systems Engineering, National Technological

Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol.12, No.2/3/4, November 2023
12
friendly interaction and that mimics human interactions, to offer a satisfactory user experience
[4].
Another consideration is choosing the right technology to understand natural language. Essential
for good chatbot performance[2]. Previous research has highlighted the importance of using
artificial intelligence techniques such as machine learning to improve chatbot learning and its
adaptability when interacting with users.
Developing an effective customer service chatbot requires not only advanced tools and
technology implementation, but also a deep understanding of user needs and expectations.
Through a comprehensive review of the literature, methodological approaches, artificial
intelligence techniques and quality attributes relevant to the successful implementation of the
chatbot will be identified.
2. THEORETICAL FRAMEWORK
2.1. Chatbot
Chatbot is an application that simulates human conversation in a chat interface. It uses advanced
artificial intelligence and natural language processing techniques to understand and provide
automated responses to user queries. Chatbots find applications in a variety of scenarios,
including customer service, help desk, sales, and marketing. They offer a convenient and efficient
way to interact with users, simulating a human-like conversation while taking advantage of
intelligent algorithms and language processing capabilities.
The historical evolution of chatbots is essential to understand their development and current
applications. Several studies have investigated this evolution and offer an overview of significant
milestones and advances in this field [1]. From early rule-based systems to sophisticated AI-
based chatbots, there has been a remarkable growth in the power and versatility of chatbots [2].
2.2. Types of Chatbots
Chatbots are classified into different types based on their features and functionality. Below are
the main types of chatbots:
1. Rule-based chatbots. Work by applying predefined instructions and providing
predetermined responses based on specific input patterns. These chatbots are efficient in
situations where the queries are clear, and a limited set of responses are available [3] .
However, its limitation lies in the difficulty of dealing with ambiguous or complex queries.
2. Chatbots based on Artificial Intelligence. Leveraging methods like natural language
processing and machine learning, chatbots have the capacity to comprehend and generate
responses in human language [4].These AI-driven chatbots possess the ability to learn and
enhance their performance over time through interactions with users. This enables them to be
versatile in various scenarios, delivering responses that are not only more precise but also
contextually relevant.
3. Voice chatbots. Are designed to interact using voice commands. These chatbots use speech
recognition and synthesis technologies to understand and generate spoken responses [6] .
Voice chatbots are especially useful in situations where the use of hands or vision is limited,
such as in automotive applications or smart home devices.
4. Hybrid chatbots. Combine features of rule-based and AI-based chatbots. These chatbots use
predefined rules for common cases and resort to artificial intelligence techniques in more

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complex situations [5]. This blend of capabilities enables a higher degree of adaptability and
receptiveness in customer service.
Every variety of chatbot comes with its own set of advantages and constraints, and the selection
hinges on the requirements and goals of the company.
2.3. Natural Language Processing
Natural language processing (NLP) is a fundamental field in the development of intelligent
chatbots. Various approaches and models related to NLP have been proposed, such as
transformer-based models, which have shown excellent results in understanding and generating
natural language [7] .
These models use tokenization, attention, and decoding techniques to improve the chatbots'
ability to understand queries and generate appropriate responses.
On the other hand, machine learning plays an important role in the development of intelligent
chatbots for fluid and contextual conversations [8]. Machine learning techniques are used for
consistent response generation, user intent detection, and dialog personalization. These
approaches allow chatbots to adapt to the preferences and needs of users, thus improving the
quality of interaction.
2.4. WhatsApp
Is an instant messaging application that enables users to send text messages, initiate voice, and
video calls, share various files and multimedia content, as well as engage in group conversations.
It was developed as a communication platform for smartphones and has become one of the most
popular messaging apps in the world.
2.5. Integration of Chatbots in Customer Service
The effective integration of chatbots in customer service is an important aspect to consider.
Strategies and best practices are explored to implement chatbots in different customer service
channels, such as online chat, social networks, and mobile applications [8]. In addition, the
personalization of responses is key to providing a more satisfactory experience, adapting
interactions to the individual preferences and needs of each customer. Finally, it matters
3. METHODOLOGY
For the development of this research, we used [26] guide, which establishes three important parts.
1. Planning. This phase is important to consider the requirements for carrying out the literature
review, considering the information search sources, the research questions and the search
criteria.
2. Conduct of the review. In this phase, the methodological selection of the information from
the main studies is carried out according to the inclusion and exclusion criteria.
3. Results of the review. In this phase, the statistical results of the studies selected for the
literature review in each of the information sources are presented in summary. These results
will serve for our research proposal.

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To develop this research, a literature review of scientific articles no older than 5 years was carried
out, extracted from important scientific databases such as Science Direct, Springer Link, Emerald
Insight, IOP science and Taylor & Francis Online.
To learn better about the types of interaction with users, the AI techniques and algorithms used,
the attributes, the technologies used in development, the mechanisms for training chatbot data,
we review articles from different authors of research related to chatbots. In the area of education,
it was necessary to ask the research questions indicated below:
Q1 What are the types of user interaction with the chatbot?
Q2 What artificial intelligence techniques and algorithms will be employed in the chatbots
development?
Q3 What will be the quality attributes of the chatbot?
Q4 Which technologies will be utilised for the chatbots development?
Q5 What mechanism will be adopted to train the chatbot data?
According to the articles identified in the literature review process according to the established
search string, the articles were filtered according to the inclusion and exclusion criteria of Table
1, resulting in many of them excluded for not meeting the established criteria. Other articles were
excluded because they did not contribute significantly to our research.
Table 1. Inclusion and exclusion criteria
Inclusion criteria Exclusion criteria
Articles published from 2018 to 2023 Articles that are not in the 2018, 2023 range
Articles in English or Spanish Articles written in languages other than
English or Spanish
Articles related to chatbots for
customer service
Other issues unrelated to customer service
The search string was extensively designed using key terms related to chatbots, user interaction,
artificial intelligence techniques, quality attributes and the technologies used in its development.
Searches were performed on different combinations of implement chatbots in different customer
service channels, such as online chat, social networks, and mobile applications [8]. Furthermore,
the benefits and Keywords.
Table 2. Search string applied in the databases.
Databases Search Strings
Science Direct natural language processing, chatbot implementation, intelligent
conversational agents, intelligent conversational agents
SpringerLink natural language processing, chatbot development, conversational AI
Emerald Insight natural language processing, chatbot applications, conversational agents
IOPscience natural language processing, chatbot algorithms, intelligent dialogue
systems
Taylor & Francis
Online
natural language processing, chatbot evaluation, conversational interfaces
After applying the inclusion and exclusion criteria, a total of forty-eight potential studies were
obtained that could provide relevant information to answer the research questions posed in the

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study. These studies were carefully examined, reviewing their titles, abstracts, and the full
content of each article.
From this review process, thirty-five relevant studies were identified that directly addressed the
research questions and provided valuable information on the types of user interaction with
chatbots, AI techniques and algorithms used, chatbot quality attributes, the technologies used in
its development and the data training mechanisms.
Finally, twenty-five selected studies were considered based on their relevance to the research
topic, the solidity of their methodology and their valuable contributions to the field of study.
Table 3. Articles found and selected by source consulted.
Database Potential
studies
Relevant
studies
Selected
studies
%
Science Direct 15 15 15 56%
SpringerLink 12 10 5 19%
Emerald Insight 6 3 2 7%
IOPscience 6 3 2 7%
Taylor & Francis Online 9 6 3 11%
TOTAL 48 35 25 100%
4. DESCRIPTION OF THE RESULTS
4.1. Types of user Interaction with Chatbot
To answer this question, we consulted several articles that examine the types of user interaction
with chatbots. Among them are the following:
The user communicates with the chatbot by sending text messages and receiving responses in text
form. This form of interaction is widely used in chatbots, as it is simple and accessible to most
users, according to the study by [24].
The user communicates with the chatbot using voice commands and receives spoken responses.
This form of interaction has become more popular with the advancement of voice recognition
technology, according to the study by [16].
The chatbot allows the user to interact through text entry and voice commands, providing the
flexibility to choose the user's preferred method, as indicated in the study by [20].
The chatbot presents predefined options in the form of buttons or drop-down menus, allowing the
user to select an option and receive responses according to their choice, as mentioned in the study
by [21].
The user asks the chatbot specific questions and receives direct answers related to the query. This
type of interaction focuses on getting clear and direct answers to the user questions, as mentioned
in the study by [2].
These reviewed articles provide a solid foundation for understanding the different forms of user
interaction with chatbots, allowing us to recognise and understand the basic characteristics of the
interaction, which can be very valuable when creating our chatbot.

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4.2. Artificial Intelligence Techniques and Algorithms Employed in the Chatbots
Development
Techniques and algorithms are vital for creating an efficient chatbot. Several pertinent techniques
and algorithms are identified from the analysis of related literature.
[1] suggest using techniques such like intent matching, machine learning, and natural language
processing.
Additionally, the study conducted by [2] highlights the use of metamodels and natural language
processing.
These results demonstrate the importance of using techniques and algorithms such as machine
learning, intention classification, and sentiment analysis to enhance chatbot efficiency and
precision.
4.3. Chatbot Quality Attributes
To identify quality attributes of the chatbot, we analysed numerous studies on chatbot usability
and user experience.
The use of chatbots to support educational systems was identified, as mentioned in the study by
[6], highlights the importance of usability, ease of use and user satisfaction. Furthermore, article
[14] mentions the impact of "humanizing" chatbots to improve user satisfaction.
These articles offer a strong basis for examining quality attributes such as effectiveness,
efficiency, usability, user satisfaction, and responsiveness when developing chatbots.
4.4. Technologies for the Development of Chatbots
Selecting suitable technologies is a crucial factor in the successful development of a chatbot. By
analysing the relevant articles, the key technologies used in chatbot development are identified.
Article [1], discusses the utilization of neural networks, natural language processing, machine
learning, and chatbots. Moreover, the study by Abdellatif et al. [2] highlights the use of natural
language understanding platforms for the development of chatbots.
These findings highlight the relevance of technologies like neural networks, natural language
processing, and machine learning in the creation of efficient and effective chatbots.
4.5. The Mechanism for Training the Chatbot Data
The process of training chatbot data is fundamental to achieving high accuracy and performance
of a chatbot. By reviewing the relevant articles, different mechanisms used to train data in chatbot
development are identified.
In particular, [24] provide a comparison of natural language understanding platforms for
chatbots in software engineering. They analysed the performance of different platforms using
supervised learning techniques and evaluated their ability to understand user queries in the
context of software engineering. Additionally, [16] explored the effects of AI-based chatbots on
user compliance in customer service. They employed a supervised learning approach to train the
chatbots and evaluated their impact on user behaviour and satisfaction, particularly concerning
adherence to the instructions provided by the chatbot.

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[17] conducts a comparative analysis of the performance of a multimodal chatbot implementation
that utilises on news classification via categories.
5. ANALYSIS OF THE RESULTS
5.1. Types of User Interaction with the Chatbot
We can examine in greater detail the percentages associated with each type of interaction, as
shown in Figure 1.
Interaction I1, which is based on the use of text, represents 37.5% of the total interactions
studied. This remarkable figure underscores the prevalence of textual communication in the
context of chatbots. The authors related to this interaction are: [24], [14] and [20].
Interaction I2, which involves the use of voice, constitutes 12.5% of the interactions. This finding
highlights the growing adoption of speech recognition technology and its integration into chatbot
systems. The author related to this interaction is [16].
Interaction I3, which combines the use of text and voice, also occupies 12.5% of the analysed
interactions. This convergence of communication modalities demonstrates the importance of
offering multiple and flexible options to users. The author related to this interaction is [20].
The I4 Interaction, based on the use of buttons, also represents 12.5% of the interactions. This
result suggests the relevance of an intuitive and simplified user interface. The author related to
this interaction is [13].
Interaction I5, which is based on a question-answer model, also shows a significant presence,
representing 25% of the interactions studied. This emphasizes the importance of chatbots' ability
to provide accurate and relevant responses to user queries. The authors related to this interaction
are [22]and [13].
Figure 1. Types of interaction
5.2. Artificial Intelligence Techniques and Algorithms Used
Figure 2 shows the results found of the AI techniques and algorithms that are used to develop
chatbots.

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The Decision Trees technique as an algorithm represents 25% of the techniques used in chatbots.
This indicates that the authors [24] and [14] have recognized the importance of using decision
trees in the development of their chatbots. This finding suggests that these algorithms are
effective in making decisions and generating appropriate responses for users.
The natural language processing (NLP) technique also represents 25% of the techniques used.
This indicates that the authors [24] and [16] recognize the importance of understanding and
processing natural language to achieve effective communication with users. This technology is
crucial to understanding queries and generating consistent and meaningful responses.
The Support Vector Machines technique as an algorithm represents 12.5% of the techniques used.
This implies that the author [21] has explored the use of these algorithms in their chatbots.
Support Vector Machines are renowned for their capability to classify and analyse intricate data,
a feature that could prove advantageous in the realm of chatbots.
The Recurrent Neural Networks (RNN) technique also represents 25% of the techniques used.
This indicates that the authors [24] and [16] have recognized the utility of RNNs in the
development of chatbots. Recurrent Neural Networks are recognized for their capacity to handle
data streams, a characteristic that holds relevance in user conversation contexts.
The Markov Chain technique and Long Short-Term Memory (LSTM) as algorithms represent
12.5% of the techniques used.
Figure 2. Artificial intelligence techniques and algorithms
5.3. Chatbot Quality Attributes
Several articles focused on the user experience and usability of chatbots have been analysed.
Figure 3 provides a more detailed look at the specific attributes to consider when developing
chatbots.
Naturalness: 42.86% of the authors have addressed naturalness in their research. This means that
they have researched and considered the importance of chatbots being able to generate responses
and conversations that are as natural and human as possible. This attribute seeks that users
perceive the chatbot as an entity with which they can interact in a fluid and natural way.
Speed: Regarding speed, it is observed that an author [16] has considered this attribute in his
research. Speed, in the context of chatbots, pertains to their capability to deliver prompt and

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efficient responses to user inquiries. A fast chatbot can enhance the user experience by furnishing
information in a timely fashion.
Availability: One author[14] has addressed availability in research on it. Availability pertains to
the chatbot's capacity to be accessible and ready for users at any given time. This means that the
chatbot is available to answer questions and help at any time of the day.
Precision: Another author [13] has considered the precision in the investigation of it. Accuracy
refers to the chatbot's ability to provide correct and exact answers. An accurate chatbot is capable
of correctly understanding user queries and delivering accurate and relevant responses.
Learning: Lastly, it is important to mention that one of the authors [1] has explored the aspect of
learning in their research.
Figure 3. Quality attributes
5.4. Technologies for the Development of Chatbots
The following technologies are among the primary ones used in the development of chatbots, as
shown in Figure 4.
Python: This technology, mentioned by the author [24] represents 10% of the total technologies
used in chatbot development. Python is a widely used programming language in the realm of
artificial intelligence and natural language processing, making it a popular choice for
implementing chatbots.
Dialogflow: This technology, mentioned by the authors [24] and [20],represents 20% of the
technologies used.
Dialogflow is a cloud-based chatbot development platform that provides advanced natural
language processing and intent understanding capabilities.
Keras: This technology, mentioned by the author [24] represents another 20% of the technologies
used. Keras is a high-level library for constructing and training neural networks, commonly
employed in deep learning and natural language processing.

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IBM Watson: This technology, mentioned in articles [24] and [19], represents a percentage of
20%. IBM Watson is an artificial intelligence and machine learning platform that offers a wide
range of services for the development of chatbots and other AI-based applications.
Twilio: This technology, mentioned in articles [24], [17] and [1], also represents the highest
percentage with 30% of the technologies used.
Figure 4. Development technologies
5.5. The Mechanism for Training the Chatbot Data
The results of the most appropriate mechanisms for data training in a chatbot can be seen in
Figure 5.
Supervised Learning: 30% of the authors have addressed supervised learning in their research.
[24] have used this approach in their work. Supervised learning entails training the chatbot using
a labeled dataset, in which instances of anticipated input and output are provided. This allows the
chatbot to learn to generate correct responses based on previous patterns and examples.
Reinforcement Learning: 10% of authors have explored reinforcement learning in their studies.
[1] have investigated this approach in their work. Reinforcement learning involves the chatbot
interacting with the environment and receiving feedback in the form of rewards or penalties.
Through feedback, the chatbot learns to make decisions that maximize rewards over time.
Transfer of Learning: 10% of the authors have considered the transfer of learning in their
research. [20] have mentioned this approach in their work. Transfer of learning involves drawing
on a model trained on a task's prior knowledge and experience and applying it to a related but
different task. This expedites the training process and enhances the chatbot's performance in the
new task.
Generation of Synthetic Data: 10% of the authors have addressed the generation of synthetic data
in their studies. In this case, a specific author has not been provided. Synthetic data generation
involves creating artificially generated training data to increase the number and diversity of
examples available to the chatbot. This can improve the chatbot's ability to generalize and handle
a variety of situations.
Active Learning: 40% of the authors have investigated active learning in their work. [24] mention
this approach in their article.

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Figure 5: Data training mechanism
6. PROPOSED ARCHITECTURE FOR CHATBOT
The architecture of the system implemented in this project comprises three levels, as shown in
Figure 6.
First level: involves the WhatsApp client application that the user will use to access the chatbot,
Second level: We have the AI engine built with python and the Flask framework using natural
language processing.
Third level: We have the Twilio-based REST API for communication between the front-end
application and the AI engine.
The customer service module is responsible for interacting with users through the WhatsApp
application. It uses an artificial intelligence engine that leverages natural language processing to
successfully understand and respond to queries [1].
The backend module is divided into two submodules. The first submodule consists of a Twilio
webhook application that enables communication between the front-end application and the AI
engine, located in the second backend submodule [2]. This AI engine uses natural language
processing algorithms and techniques implemented in a Python application. For the development
of the system, backend frameworks such as Django or Flask are used, which facilitate the
implementation of projects of this type [3].
The data set used contains information about the service company and is organized with common
words, possible intentions, and the corresponding responses. Natural language processing
techniques, such as a bag of words, are applied to count the frequency of words in the data set. In
addition, pre-processing tasks such as tokenization and removal of symbols and special characters
are performed. To build the natural language model, tools such as TensorFlow and Keras [4] are
used.
The chatbot solution to satisfy customer inquiries requires an internet connection and an Android
mobile device with the WhatsApp app installed. Users initiate the query flow by typing the
number provided by Twilio [5].
The AI engine employs a fully connected neural network architecture, known as dense layers, for
intent classification. Dense layers are used with the ReLU (Rectified Linear Unit) activation
function to learn patterns and nonlinear representations in the input data. The model is trained

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using the SoftMax activation function in the output layer to assign probabilities to each intention
class [6]. The chatbot architecture consists of client, AI, and REST API layers. The interaction is
done through the WhatsApp application and the AI engine processes the queries using natural
language processing techniques. The natural language model is constructed using dense layers
within a neural network, employing the SoftMax activation function for intent classification.
Figure 6: Proposed architecture for the chatbot
7. CONCLUSIONS
In our research, we reviewed several studies related to chatbots that provide customer service,
focusing on their efficiency, customer satisfaction, and the quality of the service they provide.
The results revealed that chatbots based on natural language processing improve customer service
efficiency by providing fast and accurate responses.
Chatbots were also found to contribute to customer satisfaction by providing quick and accurate
solutions to their problems.
The studies found have provided information on the importance of investing in technologies and
tools that support natural language processing and artificial intelligence, as well as user-centred
design to improve user experience and satisfaction.
According to the literature review, a chatbot architecture has been proposed through WhatsApp
that is friendly and personalized to achieve a positive interaction with the user.
REFERENCES
[1] A. Chaidrata et al., “Intent Matching based Customer Services Chatbot with Natural Language
Understanding,” 2021 5th International Conference on Communication and Information Systems,
ICCIS 2021, pp. 129–133, 2021, doi: 10.1109/ICCIS53528.2021.9646029.
[2] J. S. Chen, T. T. Y. Le, and D. Florence, “Usability and responsiveness of artificial intelligence
chatbot on online customer experience in e-retailing,” International Journal of Retail and
Distribution Management, vol. 49, no. 11, pp. 1512–1531, Oct. 2021, doi: 10.1108/IJRDM-08-
2020-0312/FULL/XML.

23
[3] B. A. Eren, “Determinants of customer satisfaction in chatbot use: evidence from a banking
application in Turkey,” International Journal of Bank Marketing, vol. 39, no. 2, pp. 294–311, Mar.
2021, doi: 10.1108/IJBM-02-2020-0056/FULL/XML.
[4] K. Klein and L. F. Martinez, “The impact of anthropomorphism on customer satisfaction in chatbot
commerce: an experimental study in the food sector,” Electronic Commerce Research, pp. 1–37,
May 2022, doi: 10.1007/S10660-022-09562-8/FIGURES/5.
[5] L. Nicolescu and M. T. Tudorache, “Human-Computer Interaction in Customer Service: The
Experience with AI Chatbots—A Systematic Literature Review,” Electronics 2022, Vol. 11, Page
1579, vol. 11, no. 10, p. 1579, May 2022, doi: 10.3390/ELECTRONICS11101579.
[6] C. V. Misischia, F. Poecze, and C. Strauss, “Chatbots in customer service: Their relevance and
impact on service quality,” Procedia Comput Sci, vol. 201, no. C, pp. 421–428, 2022, doi:
10.1016/J.PROCS.2022.03.055.
[7] L. Jenneboer, C. Herrando, and E. Constantinides, “The Impact of Chatbots on Customer Loyalty: A
Systematic Literature Review,” Journal of Theoretical and Applied Electronic Commerce Research
2022, Vol. 17, Pages 212-229, vol. 17, no. 1, pp. 212–229, Jan. 2022, doi:
10.3390/JTAER17010011.
[8] J. Chen, F. Guo, Z. Ren, M. Li, and J. Ham, “Effects of Anthropomorphic Design Cues of Chatbots
on Users’ Perception and Visual Behaviors,” https://doi.org/10.1080/10447318.2023.2193514,
2023, doi: 10.1080/10447318.2023.2193514.
[9] P. Suta, X. Lan, B. Wu, P. Mongkolnam, and J. H. Chan, “An Overview of Machine Learning in
Chatbots,” 2020, doi: 10.18178/ijmerr.9.4.502-510.
[10] K. Palasundram, N. Mohd Sharef, K. A. Kasmiran, and A. Azman, “Enhancements to the Sequence-
to-Sequence-Based Natural Answer Generation Models,” IEEE Access, vol. 8, pp. 45738–45752,
2020, doi: 10.1109/ACCESS.2020.2978551.
[11] J. J. Y. Zhang, A. Følstad, and C. A. Bjørkli, “Organizational Factors Affecting Successful
Implementation of Chatbots for Customer Service,”
https://doi.org/10.1080/15332861.2021.1966723, vol. 22, no. 1, pp. 122–156, 2021, doi:
10.1080/15332861.2021.1966723.
[12] P.-C. Lin and N. Arbaiy, “Baghdad Science Journal User-Oriented Preference Toward a
Recommender System,” vol. 18, no. 1, pp. 2411–7986, 2021, doi:
10.21123/bsj.2021.18.1(Suppl.).0746.
[13] M. Chkroun and A. Azaria, “A Safe Collaborative Chatbot for Smart Home Assistants,” Sensors
2021, Vol. 21, Page 6641, vol. 21, no. 19, p. 6641, Oct. 2021, doi: 10.3390/S21196641.
[14] Y. M. Çetinkaya, İ. H. Toroslu, and H. Davulcu, “Developing a Twitter bot that can join a
discussion using state-of-the-art architectures,” Soc Netw Anal Min, vol. 10, no. 1, pp. 1–21, Dec.
2020, doi: 10.1007/S13278-020-00665-4/METRICS.
[15] C. Rzepka, B. Berger, and T. Hess, “Voice Assistant vs. Chatbot – Examining the Fit Between
Conversational Agents’ Interaction Modalities and Information Search Tasks,” Information Systems
Frontiers, vol. 24, no. 3, pp. 839–856, Jun. 2022, doi: 10.1007/S10796-021-10226-5/TABLES/7.
[16] M. Adam, M. Wessel, and A. Benlian, “AI-based chatbots in customer service and their effects on
user compliance,” Electronic Markets, vol. 31, no. 2, pp. 427–445, Jun. 2021, doi: 10.1007/S12525-
020-00414-7/FIGURES/7.
[17] P. Anki, A. Bustamam, and R. A. Buyung, “Comparative Analysis of Performance between
Multimodal Implementation of Chatbot Based on News Classification Data Using Categories,”
Electronics 2021, Vol. 10, Page 2696, vol. 10, no. 21, p. 2696, Nov. 2021, doi:
10.3390/ELECTRONICS10212696.
[18] S. Schanke, G. Burtch, and G. Ray, “Estimating the Impact of ‘Humanizing’ Customer Service
Chatbots,” https://doi.org/10.1287/isre.2021.1015, vol. 32, no. 3, pp. 736–751, May 2021, doi:
10.1287/ISRE.2021.1015.
[19] G. A. Santos, G. G. de Andrade, G. R. S. Silva, F. C. M. Duarte, J. P. J. Da Costa, and R. T. de
Sousa, “A Conversation-Driven Approach for Chatbot Management,” IEEE Access, vol. 10, pp.
8474–8486, 2022, doi: 10.1109/ACCESS.2022.3143323.
[20] S. Borsci et al., “The Chatbot Usability Scale: the Design and Pilot of a Usability Scale for
Interaction with AI-Based Conversational Agents,” Pers Ubiquitous Comput, vol. 26, no. 1, pp. 95–
119, Feb. 2022, doi: 10.1007/S00779-021-01582-9/TABLES/9.
[21] G. Caldarini, S. Jaf, and K. McGarry, “A Literature Survey of Recent Advances in Chatbots,”
Information 2022, Vol. 13, Page 41, vol. 13, no. 1, p. 41, Jan. 2022, doi: 10.3390/INFO13010041.

24
[22] I. K. F. Haugeland, A. Følstad, C. Taylor, and C. Alexander, “Understanding the user experience of
customer service chatbots: An experimental study of chatbot interaction design,” Int J Hum Comput
Stud, vol. 161, p. 102788, May 2022, doi: 10.1016/J.IJHCS.2022.102788.
[23] D. Zumstein and S. Hundertmark, “Chatbots-An Interactive Technology for Personalized
Communication, Transactions and Services,” IADIS International Journal on WWW/Internet, vol.
15, no. 1, pp. 96–109, Accessed: Jul. 15, 2023. [Online]. Available:
https://www.researchgate.net/publication/322855718
[24] A. Abdellatif, K. Badran, D. E. Costa, and E. Shihab, “A Comparison of Natural Language
Understanding Platforms for Chatbots in Software Engineering,” IEEE Transactions on Software
Engineering, vol. 48, no. 8, pp. 3087–3102, Aug. 2022, doi: 10.1109/TSE.2021.3078384.
[25] O. Makhkamova and D. Kim, “A Conversation History-Based Q&A Cache Mechanism for Multi-
Layered Chatbot Services,” Applied Sciences 2021, Vol. 11, Page 9981, vol. 11, no. 21, p. 9981,
Oct. 2021, doi: 10.3390/APP11219981.
[26] Kitchenham, B. and Charters, S. (2007) Guidelines for Performing Systematic Literature Reviews in
Software Engineering, Technical Report EBSE 2007-001, Keele University and Durham University
Joint Report. - References - Scientific Research Publishing.
https://www.scirp.org/(S(oyulxb452alnt1aej1nfow45))/reference/ReferencesPapers.aspx?ReferenceI
D=1555797 (accessed Sep. 23, 2023).

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