Bibliometric Insights into Web Scraping and Advanced AI-Based
Models for Valuable Business Data
Barba Giuliana
, Lazoi Mariangela
and Lezzi Marianna
Department of Engineering for Innovation, University of Salento, Campus Ecotekne Via Monteroni, Lecce 73100, Italy
Keywords: Web Scraping, Artificial Intelligence, Natural Language Processing, Business Data Analysis, Sentiment
Abstract: The integration of advanced Artificial Intelligence (AI) based models with web scraping technique opens new
opportunities for businesses, streamlining the extraction of valuable insights from the huge amounts of online
data. This integration is strategic in overcoming the challenges of extracting dirty data and retrieving missing
information, which could otherwise compromise the reliability of business decisions. Despite the growing
importance of integrating AI-based models and web scraping techniques in the business context, there exists
a significant gap in understanding the specific implications. To address this gap, our study uses a systematic
literature review (SLR) and bibliometric analysis to examine the implications of the combined use of advanced
AI-based models and web scraping in business contexts. The study highlights four distinct clusters that sug-
gest potential research areas in the areas of “Machine Learning (ML) for sentiment analysis”, “Artificial In-
telligence and Natural Language Processing (NLP) integration”, “Data intelligence and optimization”, “NLP
and Deep Learning (DL) integration”. The paper offers both theoretical and practical contributions, providing
a clear overview of emerging research directions in the field of AI-based models and web scraping integration
and guiding managers in adopting advanced AI-based models to enhance the value of web data obtained
through scraping.
Online data are a crucial tool for knowledge genera-
tion and knowledge-based decision support (Rejeb et
al., 2020). In particular, online data enable companies
to consolidate information by transforming it into
useful insights for marketing and service decisions,
such as optimising pricing decisions based on con-
sumer behaviour (Jorge et al., 2020) or segmenting
customers based on their perceptions (Rejeb et al.,
2020). Moreover, gathering online data not only
facilitates a comprehensive analysis of web users'
perceptions of corporate products and services
(Bisconti et al., 2019), but also contributes positively
to the enrichment of the company's information assets
(M. A. Khder, 2021).
Companies that extract insights from online data
prove to be more agile and more adaptive to market
dynamics (Rejeb et al., 2020), highlighting the crucial
importance of the strategic use of web data to drive
business decisions and maintain a distinctive compet-
itive position in the global landscape.
Web scraping is a valuable technique for auto-
matic data collection from the internet (Tanasescu et
al., 2022) about customer feedback (Bisconti et al.,
2019) and sentiment about particular products (Jorge
et al., 2020). However, web data from various online
sources such as websites, blogs and social media is
often unstructured and consists of a wide range of het-
erogeneous information such as text, images and
video (Eberendu, 2016). To enhance the value of un-
structured data, advanced Artificial Intelligence (AI)
based models can extract hidden insights from news
and opinions on the web providing useful in under-
standing customer attitudes (Chan et al., 2022). This
information can help decision-making strategies (Ta-
nasescu et al., 2022) and refining customer targeting
(Rejeb et al., 2020).
Giuliana, B., Mariangela, L. and Marianna, L.
Bibliometric Insights into Web Scraping and Advanced AI-Based Models for Valuable Business Data.
DOI: 10.5220/0012686900003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 321-328
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
The implementation of advanced AI-based mod-
els, together with the web scraping, presents novel
opportunities for companies as it facilitates the man-
agement of vast amounts of data from the internet
with the objective of extracting valuable insights (Ar-
junan, 2022). This integration is particularly im-
portant for overcoming the challenges of mining dirty
data and retrieving missing information that could
otherwise affect the reliability of business decisions
(Tanasescu et al., 2022). Several studies reflect the
growing interest in integrating advanced models
based on artificial intelligence and web scraping. For
instance, (Kumar et al., 2021) focus on estimating the
relevance of online documents; while, (Sahu et al.,
2022) address the evaluation of customer reviews in
the context of e-commerce. Moreover, (Arjunan,
2022) study highlights the potential of integrating ad-
vanced artificial intelligence-based models to enrich
web data. However, the lack of analysis on implica-
tions of integrating AI-based models and web scrap-
ing in business contexts reveals a gap that needs to be
Through a Systematic Literature Review (SLR)
and bibliometric analysis, the objective of this study
is to investigate the current key research directions in
the combined use of advanced AI-based models and
web scraping applied in business contexts. This
promises to highlight the main implications in this
emerging field and to help explore a new frontier in
the literature, which still seems to be developing. In
particular, the paper aims to provide an answer to the
following research question: "What are the implica-
tions of applying web scraping integrated with ad-
vanced AI-based models in the business context?". To
answer this research question, a network examination
and visual analysis of textual bibliographic data ac-
quired from Scopus database were conducted, with
the aim of creating a map of interconnections using
VOSviewer (a software tool for bibliometric analy-
2.1 Web Scraping and Advanced
AI-Based Models for Business
Web scraping - also known as web crawling (M. A.
Khder, 2021), web harvesting (Zhao, 2017), web data
extraction (Zhao, 2017), web data scraping (Barbera
et al., 2023), or screen scraping (Arjunan, 2022) - is
an advanced technique for systematically extracting
unstructured data from websites and subsequently
transforming them into structured data to be stored in
a file or database (M. A. Khder, 2021s).
The significant advantage of web scraping lies in
the automation of the process of searching and ex-
tracting data, eliminating the need to manually copy
information from a website to a file (M. A. Khder,
2021; Tanasescu et al., 2022). Moreover, in the con-
text of data science, web scraping emerges as a tool
of considerable scientific interest, recognized as an
efficient method for collecting big data (Barbera et
al., 2023), thanks to low execution and maintenance
costs (M. A. Khder, 2021).
The applications of web scraping span various
sectors, significantly contributing to the business and
marketing world. In particular, web scraping is
widely employed in the analysis of online user feed-
back, such as for the support of event management
(Bisconti et al., 2019), the prediction of consumers
‘choices (Corallo et al., 2020), the monitoring
changes in product prices (Jorge et al., 2020) or to in-
vestigate employee opinions (Tanasescu et al., 2022).
Furthermore, in the job search engine sector, there are
several applications of web scraping to define job
profiles (De Mauro et al., 2018) and conduct labour
market surveys (Vankevich & Kalinouskaya, 2021).
On the other hand, AI has revolutionised the business
landscape, by enabling the use of models capable of
handling an enormous stream of heterogeneous data
identifying hidden patterns or trends in large datasets
(Afandizadeh et al., 2023). The application of AI-
based models is increasingly significant in analysing
online data (such as customer reviews or sales infor-
mation) to detect behavioural patterns and identify
preferences (Chan et al., 2022).
The application of these models allows companies
to delineate detailed customer segmentation strate-
gies, opening the door to personalised marketing
campaigns, individualised product suggestions and
customer experiences tailored to specific needs
(Afandizadeh et al., 2023). Therefore, companies can
gain extensive and contextual knowledge from online
data, enabling them to make informed decisions and
develop precise business strategies (Chan et al.,
AI-based models are pivotal in audio, video, im-
age and text processing, significantly contributing to
the optimisation of diverse business operations and
demonstrating how innovative technologies have
been transformed into practical commercial solutions.
For instance, (Arslan & Cruz, 2022) use an AI-
based model to exploit large corpora of textual docu-
ments while (Sarica et al., 2020) propose the TechNet
model, demonstrating how AI can be used to extract
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
relational knowledge from complex patent docu-
ments or also to find optimal candidates facilitating
recruitment process (Sridevi & Suganthi, 2022).
AI-based models are also used to analyse images
for hot rolling mill process defect detection (Latham
& Giannetti, 2023) or to improve the performance of
harvesting robots (Tang et al., 2020).
2.2 Integration of Web Scraping and
Advanced AI-Based Models in the
Business Domain
The combination of advanced AI-based models and
web scraping presents a modern strategy for obtaining
high quality data from the web by optimizing the data
collection process, eliminating redundant information
and ensuring the delivery of more relevant and clean
data (Arjunan, 2022).
The application of this integration plays a key role
in providing crucial information for understanding
consumer sentiment, thus helping to drive informed
decisions in the business environment (Sahu et al.,
The synergies of web scraping and AI-based mod-
els integration are evident in various contexts, as
shown by (Hao et al., 2023), which extract vulnerable
online contracts using web scraping, and trained an
AI-based model to enhance the security of smart con-
tracts. (Thuan et al., 2022) use this integration to sim-
plify the assignment of professional roles based on
employee competencies. Furthermore, (Kinne & Re-
sch, 2018) propose an AI-based model that assesses
the companies’ degree of digital innovation by ana-
lysing web scraped data from their websites.
The joint use of AI-based models for sentiment
and opinion recognition, together with web scraping,
is another demonstration of how AI can enhance the
value of data collected from online posts and reviews
(M. A. Khder, 2021). A tangible example is provided
by (Kafeza et al., 2023), which exploit these data
from social media to model customers' actions in or-
der to discover their behavioural patterns. Addition-
ally, (Tanasescu et al., 2022) collect data via web
scraping on employee feedback and integrate it into
an AI-based model to facilitate decision-making pro-
cesses based on the opinions of company employees.
Moreover, other studies show that AI-based mod-
els improve the accuracy of web scraping by provid-
ing more relevant information and cleaner data. In
particular, (Reddy et al., 2021) present an AI-based
model that supports the scraping process by summa-
rizing meaningful insights of online documents. (Ar-
junan, 2022) show that AI-based model can be spe-
cialized in recognising the relevance of web and in
eliminating superfluous information. On the other
hand, (M. Lee & Na, 2023) use this integration to se-
lect relevant companies information by retrieving
customised market data from the web; whereas (Sahu
et al., 2022) propose an AI-based model to evaluate
the honesty of online scrapped reviews.
The combination of AI-based models and web
scraping holds promise, but poses significant chal-
lenges. First, further exploration in the field of AI-
based models is necessary to improve the perfor-
mance and accuracy of results as the current imple-
mentation is in its early stages of development (Sahu
et al., 2022; Tanasescu et al., 2022). Moreover, the
malleability of website design and layout presents a
significant obstacle, as scripts can quickly become
obsolete, and adapting them to hundreds of sites is
nearly impossible (Patnaik & Babu, 2021).
Furthermore, the analysis of sentiments poses
even greater challenges, as it involves identifying
complex phenomena like negation or irony in the an-
alysed texts (Tanasescu et al., 2022).
The research strategy was designed to gain the current
key research directions in the combined use of ad-
vanced AI-based models and web scraping for busi-
ness applications. In particular, the research aims to
address the key question: "What are the implications
of applying web scraping integrated with advanced
AI-based models in the business context?".
To address this question, a SLR was conducted as
the main methodology for data collection. This pro-
cess, known for its transparency, scientific rigor, and
replicability, allows for the identification, highlight-
ing, and evaluation of various sources of information,
facilitating the cataloguing and structured compari-
son of results (Del Vecchio et al., 2023; Tranfield et
al., 2003).
Additionally, a bibliometric analysis was con-
ducted to examine the collected sample and gain an
exploratory understanding of the main themes of in-
terest. Bibliometric analysis, introduced by
(Pritchard, 1969), is recognized as a crucial method-
ology for exploring research in various disciplines,
highlighting its nature, and providing particularly val-
uable insights in fields still in development (Donthu,
Kumar, Mukherjee, et al., 2021). As suggested by
(Del Vecchio et al., 2023), the research process is
structured into three main phases: definition of the
search framework and data sample, preliminary anal-
ysis of the sample, and data analysis.
Bibliometric Insights into Web Scraping and Advanced AI-Based Models for Valuable Business Data
3.1 Definition of Search Schema and
Data Sample
In line with the objective of this study and following
(Corallo et al., 2021) SRL procedure, the keywords
were identified for the investigation of the fields of
interest: web scraping, artificial intelligence, text
mining and business.
The choice of these keywords was based on the
consideration of similar concepts, synonyms, and ac-
ronyms identified in the theoretical context. The com-
bination of these keywords was implemented using
accurate mathematical logical connectors (Boolean
and Proximity operators) as it is provided in Table 1.
Table 1: Query structure used for Scopus.
("web") W/2 ("scrap*" OR "craw*" OR "harvest*" OR
"data extract*" OR "data harvest*" OR "data scrap*" OR
"automat* scrap*" OR "information extract*")) OR
(“screen scrap*" OR "automat* web scrap*" OR "spi-
("Machine learning" OR "ML" OR "artificial intelli-
gence" OR "AI" OR "natural language process*" OR
"NLP" OR "natural language generat*" OR "NLG" OR
"artificial neural network" OR "ANN" OR "neural net-
work" OR "deep neural network" OR "DNN" OR "recur-
rent neural network" OR "RNN" OR "unsupervised
learn*" OR "convolutional neural network" OR "CNN"
OR "supervised learn*" OR "learning algorithm" OR
"convolutional neural network" OR "CNN" OR "SVM"
OR "support vector machine")
("text data" OR "Text mining" OR "data mining" OR
"web mining" OR "web data mining" OR "knowledge dis-
covery from text" OR "KDT" OR "information extrac-
tion" OR "IE" OR "information retrieval" OR "IR" OR
"data enrich*" OR "data augment*" OR "data extract*"
OR "data collect*" OR "semantic analysis" OR "senti-
ment analysis" OR "sentiment recognition" OR "text anal-
ysis" OR "data analysis" OR "information retrieval" OR
"IR" OR "big data" OR "Text Summarization" OR "Text
Generation" OR "Market Intelligence" OR "Predictive
Analytics" OR "Reputation Monitoring")
("Business" OR "firm" OR "enterprise" OR "company"
OR "organization" OR "manufactur*" OR "industry" OR
"market*" OR "account*" OR "sales" OR "decision mak-
ing" OR "knowledge manag*" OR "strategic planning"
OR "financial" OR "HRM" OR "human resource
manag*" OR "SCM" OR "supply chain manag*" OR "risk
management" OR "retail" OR "CRM" OR "customer re-
lationship manag*" OR "insurance")
The query used for Scopus (Table 1) was modified
in the field tag and proximity operator to adapt for
Web of Science (WOS).
Papers containing the keywords in the title, ab-
stract, and keywords were searched in the Scopus and
Web of Science electronic scientific database in De-
cember 2023, chosen for their robustness as biblio-
graphic data sources (Pranckutė, 2021). Subsequently
the initial sample of 455 and 129 articles, respectively
from Scopus and WOS, was refined to 459 unique by
setting English language limitation as the only filter.
All results were exported in RIS format, retaining
all necessary information for conducting the subse-
quent analysis, including title, year, abstract, and key-
3.2 Preliminary Analysis of the Sample
The second step of the adopted methodology focuses
on the statistical analysis through MS Excel of
metadata associated with the selected papers. Ini-
tially, we explored the quantity of publications over
the years (Figure 1). The graph reveals that from 1998
to 2008, the production of studies in this field is neg-
ligible. However, from 2009 to 2017, a gradual in-
crease occurs, indicating a growing interest in data
management and its connection to the web within the
scientific community. It's interesting to note that from
2017 to 2023, there is a rapid growth in the number
of studies related to this domain, with a remarkable
growth rate of 100% over six years.
Additionally, the distribution of papers based on
their respective disciplinary areas was examined.
"Computer Science" emerges as the predominant
area, representing 36.5% of the total articles pub-
lished on this subject. Following in order are the dis-
ciplinary areas of "Engineering" (15.4%), and "Deci-
sion Science" (10.3%), confirming the multidiscipli-
nary nature and broad scope of web scraping and AI-
based models in the business context.
Figure 1: Distribution of papers by year.
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
3.3 Data Analysis
In the third phase of this research methodology, an in-
depth bibliometric analysis has been chosen as a suit-
able method for macro-level evaluations of the 459
sample of selected papers. Additionally, since it is not
influenced by researcher subjectivity, it can help to
decrease reviewer bias during research (van Oorschot
et al., 2018).
In the context of this study, bibliometric analysis
played a crucial role in delineating implications and
future research directions related to combined use of
AI-based models and Web Scraping within business
Specifically, through this analysis, the objectives
were to: i) comprehend the most recurring topics in
the fields of analysis; ii) identify relationships among
them; iii) highlight emerging themes and trends over
time. Network analysis and graphical exploration of
textual data were conducted using VOSviewer soft-
ware, known for its ability to create easily interpreta-
ble co-occurrence maps on a large scale (van
Oorschot et al., 2018). Following the guidelines of
(Donthu, Kumar, Mukherjee, et al., 2021), the analy-
sis focused on counting recurring terms in title, key-
words and abstracts, with a minimum frequency of
10. By carrying out a preliminary merge of the ex-
tracted keywords and creating a specific thesaurus,
out of the 3658 terms examined, 67 meet the thresh-
old, contributing to a clear definition of conceptual
relationships within papers keywords.
For the data analysis phase, VOSviewer offers three
visualizations, namely the network, overlay, and den-
sity visualizations which are commented in next sec-
4.1 Network Visualization
Bibliometric analysis enables a network analysis that
relies on term co-occurrence. The co-occurrence map,
presented in Figure 2, clearly displays four main clus-
ters generated by binding a minimum of one item per
Cluster 1 (red), comprising 22 terms, focuses on the
implementation of “Machine Learning (ML) for senti-
ment analysis”, with an emphasis on big data, learning
algorithms, web scraping and online social networks.
Additionally, the topics cover aspects of financial mar-
kets, risk assessment, e-commerce, investment, data
analysis, and human impacts. This implies an interest
in comprehending human and market dynamics using
sophisticated data analysis techniques.
Cluster 2 (green), consisting of 19 terms, ad-
dresses the “AI and NLP integration” to optimise the
management and analysis of online information. Key
topics include classifying information, semantics,
search engines, extracting web information and man-
aging knowledge, with a specific emphasis on ontol-
ogies and semantic analysis.
Cluster 3 (blue), with 16 terms, focuses on “Data
intelligence and optimization” to improve the extrac-
tion and management of information through data
mining and text mining approaches. This indicates an
emphasis on improving data collection and manipula-
tion operations, alongside decision-making systems.
Cluster 4 (yellow), consisting of 10 terms, focuses
on the “NLP and DL integration”, such as deep neural
networks and convolutional neural networks. This
cluster highlights an interest in advanced natural lan-
guage comprehension to facilitate informed decision-
making, as well as emphasizing the analysis of opin-
ions through deep learning techniques.
Moreover, the position of the largest nodes in Fig-
ure 2 confirms that the extraction and analysis of data,
both structured and unstructured, are crucial aspects
for research in the considered field. Furthermore, the
presence of these central nodes suggests that technol-
ogies, such as web scraping, data mining and text
mining, are strongly integrated with big data and ma-
chine learning, indicating an emphasis on extraction
and analysis of web data as an integral part of the de-
cision-making process and knowledge generation.
Figure 2: Network visualization.
Bibliometric Insights into Web Scraping and Advanced AI-Based Models for Valuable Business Data
4.2 Overlay Visualization
The overlay map (Figure 3), a variant of the co-occur-
rence map, broadens the temporal perspective by in-
troducing a distinctive chronological dimension. The
use of distinct colours highlights the older terms in
blue, intermediate terms in green, and more recent
terms in yellow. This temporal representation spans
from 2014 to 2022. The choice of this time interval is
crucial as it provides an optimal balance for clearly
perceiving variations in terms over the years.
It is interesting to note that the older terms, cover-
ing the period from 2014 to 2017, such as information
retrieval, information extraction, semantic web, and
AI, reflecting the early stages of development when the
focus was on information organisation and extraction.
Over the interim period from 2017 to 2019, there
has been a move towards more research in NLP,
learning systems, web crawling and data mining. This
suggests an awareness of the increasing significance
of online information and a drive to create more so-
phisticated systems to handle it.
Finally, the recent terms, from 2019 to the present,
reflect the ongoing development of technologies and
areas of interest. Terms such as web scraping, deep
learning, sentiment analysis, social media, and con-
volutional neural networks highlight a greater empha-
sis on advanced data analysis and the application of
advanced technologies across various domains (i.e.
sales, financial, risk assessment), encompassing those
associated with online social dynamics.
Figure 3: Overlay visualization.
4.3 Density Visualization
Density visualization provides a perspective on the
distribution and relevance of terms within the exten-
sive range of analysed papers (Figure 4). Each identi-
fied label and the surrounding area, takes on a colour
and intensity that reflect the density of papers in that
specific position.
The presence of key terms such as web crawling,
data mining, AI, NLP systems, ML, sentiment analy-
sis, big data and learning systems highlights points of
dense concentration where scientific research has sig-
nificantly focused. In these clusters, the colour tends
to shift towards dark blue, indicating a higher density
and suggesting that these topics are at the centre of
academic debate.
Conversely, terms like deep learning, sales, and
knowledge management, while important, exhibit a
more widespread distribution characterized by lighter
shades of yellow, suggesting a lower concentration in
specific research areas compared to key terms. This
could indicate that these concepts are addressed in
various contexts and sectors without a specific focus
in a single research area.
The keyword density highlights central and cur-
rent topics in scientific analysis, reflecting a contin-
ued interest in crucial areas such as NLP, ML and data
mining from web sources, with a keen eye on new de-
velopments and emerging challenges.
Figure 4: Item density visualization.
The bibliometric analysis conducted offers a detailed
perspective on the main areas of research and devel-
opment in the field of web scraping and AI-based
models. The four clusters identified
(“ML for senti-
ment analysis”, “AI and NLP integration, “Data intel-
ligence and optimization”, “NLP and DL integra-
tion”) reflect different approaches of analysis and us-
age of online information. Several topics emerge
from these clusters, such as the implementation of
ML for sentiment analysis, the integration between
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
AI and NLP to optimize information management,
the use of data mining and text mining techniques to
improve data extraction and management, and the ap-
plication of advanced technologies such as deep neu-
ral networks to understand and analyse complex
online opinions. Furthermore, the temporal view
highlights a progression in research focus over the
years, with an increase in attention towards advanced
data analysis and the application of these innovative
From an academic perspective, the identification
of emerging research trends is one of the most rele-
vant contributions. By analysing the temporal distri-
bution of keywords, the evolution of topics of interest
over time can be identified, providing academics with
a clear overview of emerging research directions.
Furthermore, the analysis provides insights into the
intersections between different disciplines and re-
search fields.
From a managerial perspective, this study pro-
vides greater clarity of key trends and themes in the
field of web scraping and AI-based models, which
can inform strategic decisions regarding investments
in technologies to understand the online data and op-
timise their extraction and analysis.
However, this research has some limitations. The
interpretation of the results could be affected by the
subjectivity of the observers and their previous
knowledge in the field, introducing potential biases
into the analysis. Finally, bibliometric analysis may
not fully capture the dynamism and complexity of the
context such as socio-cultural and political factors
that could influence research trends. Future research
should integrate theoretical and empirical approaches
to obtain a more complete and in-depth understanding
of the dynamics and challenges in the field of web
scraping combined with AI-based models, with the
aim of contributing to the development of innovative
and impactful solutions.
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