Exploring Sentiment Analysis Research: A Social Media Data Perspective

International Journal on Soft Computing (IJSC) Vol.14, No.1, February 2023
DOI: 10.5121/ijsc.2023.14101 1
EXPLORING SENTIMENT ANALYSIS RESEARCH: A
SOCIAL MEDIA DATA PERSPECTIVE
Zahra Dahish1, 2
and Shah J Miah1
1
Newcastle Business School, College of Human and Social Futures, University of
Newcastle, NSW, Australia
2
Management Information System, College of Business Administration,
Jazan University, Saudi Arabia
ABSTRACT
Sentiment analysis has been rapidly employed for business decision support. New data mining researchers
are yet to have an adequate understanding of the various applications of sentiment analysis while utilising
social media data. As a result, it is critical to define the data mining and text analytics research trend
holistically using existing literature. The study explores sentiment analysis research for its application in
transforming social media data and identifies relevant research aspects through a comprehensive
bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to
discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis
are mainly related to innovation, transparency, and efficiency. Our review also highlights the
distinctiveness of sentiment analysis for synthesising social media information to investigate various
features, including the knowledge-domain map that detects author collaboration networks in the past.
KEYWORDS
Social Media, Sentiment Analysis, Bibliometric Analysis, Systematic literature review
1. INTRODUCTION
Sentiment analysis (SA) is a type of computational qualitative analysis that has proliferated over
the past decades for various business applications. The application is to learn how people feel
about things like users, groups, organisations, problems, and themes (Kumar et al., 2021;
Mehraliyev et al., 2022; Mejova et al., 2009). Researchers use sentiment analysis to
systematically categorise and extract customers' feelings about products from their discussions or
posts on social media in order to study consumer emotions in order to discover new insights into
their point of view. Questions have been raised about the accuracy and applicability of sentiment
analysis, despite the potential of the techniques for interpreting massive amounts of data gathered
in a real-world business setting (Mehraliyev et al., 2022; Prager, 2006; Ravi & Ravi, 2015).
A systematic literature review is a well-accepted research method for synthesising scientific
evidence to answer research questions or explore research issues in a transparent and rigors
manner, including all available evidence from the topic areas for assessing its rationale and
quality (Gray, 2019).The primary goal of the traditional review approach is to reduce the risk for
bias in studies and to increase transparency at every stage of the review process by relying on
explicit and systematic methods to operate appropriate selection and inclusion of studies, by
appraising quality of the included studies, and to objectively summarise the outcomes from
them (Liberati et al., 2009; Petticrew, 2001). There are many types of SLR studies. For example,
theory-based, content-analysis based, theme-based, framework-based, theory development,

2
bibliometric, theory-context-characteristics-methodology (TCCM), and meta-analysis reviews
(Aromataris & Pearson, 2014). In this study, we used a bibliometric review, which allows
researchers to collect hundreds to thousands of relevant papers on a research topic for analysis
and the generation of new results. Visualization tools like CiteSpace, Perish, and VOSviewer are
used to identify trends in literature and journal performance, collaboration patterns, and research
elements, as well as the intellectual structure of a certain subject in the literature (Donthu et al.,
2021a).
Scopus, an online research database, comprises nearly all key research journals; therefore,
relevant publications exist. It also has built-in analysis features to provide representative
representations for new generalised understanding. For further analysis, Scopus search results can
be exported using various tools, such as R-tool-biblioshiny. There is no global or comprehensive
review of social media studies or pertinent research that is directly oriented to SA, despite a rise
in humanistic and social science publications. As a result, this paper makes an important
contribution to the target body of knowledge areas because it presents in-depth bibiomatric
findings using the R-biblioshiny tool for analysing SA bibliographic data in social media studies.
In this paper, we discuss how the R-tool biblioshiny can be used to create, explore, and visualise
maps of bibliometric networks.
Consequently, the following research questions (RQs) guide our analysis:
RQ1: What are the publication and citation trends of SA in social media research?
RQ2: What are the most influential agents (documents, sources, authors, institutions, and
countries) in SA in social media research?
2. STUDY BACKGROUND- SENTIMENT ANALYSIS
SA is often referred to as "opinion mining" in the data mining research field because it is the
computing study sub-field of capturing how people feel about various things, such as entities,
persons, situations, events, topics, and their characteristics (Devika et al., 2016; Medhat et al.,
2014; Ravi & Ravi, 2015). SA is a method that uses Natural Language Processing (NLP) to
extract, transform, and understand text-based opinions before categorising them as positive,
negative, or neutral (Devika et al., 2016; Joshi & Itkat, 2014; Mathew & R, 2020). Sentiment
analysis is a crucial scientific approach for businesses to ascertain consumer sentiment insights
transforming specific views, such as on brands or products.
Since it is impossible to manually keep up with the huge flow of new information appearing on
the Internet, this sub-field (directly related to automatically extracting opinions) has significantly
evolved in the last decade (Dhana Laxmi et al., 2020; Zhang et al., 2020). SA takes into account
not only for investigating the positive or negative polarity of words and concepts, but also the
syntactical tree of the sentence (Obaidi et al., 2022; Prager, 2006). The software makes an effort
to analyse idiomatic and colloquial expressions, to provide meaning for negations, to change the
polarity of words based on their proximity to other words (such as adverbs, adjectives, and
conjunctions), and to account for certain functional-logic complements (Calefato et al., 2018;
Hausmann et al., 2020).
Sentiment analysis in social media analytics provides many opportunities, since it helps capture
emotions including denial, irony, joy, sadness, and wrath. The rapid transmission of beliefs and
ideas on social media generates a "neuralgic" reaction. Shared neurobiological pathways generate
close ties among members of a network. Our neurochemistry benefits from strong social
connections in a network. Leaders can identify issues and create solutions by analysing social
media sentiment. Assessing customers' opinions has been a vital element of corporate success.
Product and service reviews are also abundant online. Thus, companies regularly hire researchers

3
to analyse their web material for opinions. Table 1 contains some recent academic studies that
investigate novel findings from web text for opinion mining and sentiment analysis.
Table 1: Related existing studies of SA
Central topics Authors Purposes of the studies
Understanding
public opinions
(Qian et al.,
2022)
Provided a thorough analysis of public sentiments about NFTs
as expressed on Twitter and used sentiment and emotion
analysis to further investigate the secondary market and uncover
the factors contributing to NFTs' rising popularity.
Sentiment
analysis-based
expert weight
determination
method
(Wan et al.,
2021)
Determined the best way to utilise such data to facilitate
LSGDM at scale is, therefore, an area worthy of research. This
paper is the first to use social media sentiment analysis to assess
LSGDM's quality
An evaluation
of eleven SA
tools
(He et al.,
2022)
SA is used to examine the efficacy of current sentiment analysis
tools on social media datasets, with a focus on health-related
and healthcare-related topics. To fill that void is the purpose of
this research.
Topic-level SA
of social media
(Pathak et al.,
2021)
Proposed a topic-level sentiment analysis model based on deep
learning.
Time series SA
of relief
operations
(Bhullar et al.,
2022)
SA based on time series analysis for relief operations utilising
social media data with situational information gathered using
Twitter
2.1. Bibliometric Studies
We present bibliometric research in a similar vein to that which has appeared in prominent
academic journals. Bibliometrics is important tool in evaluating the scientific outputs of different
nations, regions, journals, and institutions (Tang et al., 2018). In almost every field of
information systems, there are many high-impact studies that use the bibliometric tool for
reviewing existing literature. Table 2 contains a list of relevant articles focusing on bibliometric
studies from various fields.
Table 2: Relevant studies conducting SLR with the Bibliometric analysis.
Studies Purposes of the studies
Vrontis et al.,
(2022)
Conducted SLR for bibliometric meta-analysis on societal effects of social
media applications in organizations, adopting practices of the VOSviewer
software.
Donthu et
al.,(2021b)
Providing detailed instructions for bibliometric analysis to provide context for
the use of comparison to other methods, such as meta-analysis and systematic
literature reviews.
Z. Xu, Wang, et
al., (2021)
Developing deeper insights into the realm of consumers by utilising
bibliometric analysis and visualisation techniques.
Aria &
Cuccurullo, (2017)
Using bibliometrix to conduct in-depth science mapping analysis.

4
3. SYSTEMATIC REVIEW PROCESS
The ultimate goal of a systematic review is to identify relevant research and synthesise its
findings. Annotated bibliographies that provide a scientific synthesis of the source literature are
examples of independent literature reviews. This article discusses the SLR (systematic literature
review), which is a more comprehensive literature review than solo reviews. (Lin et al., 2022;
Okoli & Schabram, 2010). SLR is an appropriate research method for the current study because
of the nature of the research questions (RQs), which aim to understand the trends and investigate
prevailing gaps in the pertinent scientific literature (Whittemore et al., 2014).
Bibliometrics is an interdisciplinary scientific approach that employs statistical and mathematical
methods to quantitatively analyse bibliographic data (Zanjirchi et al., 2019). However, one of the
most common ways to disseminate research results is through articles published in scientific
journals, which provide a representative cross-section of international scientific activity.
Finding the documents to analyse is the initial stage of a systematic review. To do this, we
created a three-step search and filtration technique for our review, which included a database
search, filtration of the database results, and the exclusion of irrelevant documents (Kumar et al.,
2021). Comparing with the PRISMA flow diagram for different phases of SLR activities, our
simplified stage one is related to the database search. We selected the databases in which to
search for documents. Multiple databases exist, including PubMed, ISI, Web of Science, Scopus,
and Dimensions. Scopus was selected because it indexes scholarly and scientifically relevant
publications. It provides extensive bibliometric data for each publication it indexes. Scopus is a
great tool for projects that compile a large body of literature for scholarly analysis (Paul et al.,
2021), It is an academic database that is highly recommended for bibliometric analysis (Donthu
et al., 2021a).
Importantly, Scopus is a more complete and high-quality data source for review, since it is a
high-quality source for bibliometric data (Baas et al., 2020) and its metrics have a "very high"
correlation with those in other scientific databases like Web of Science (Liu, 2013). Stage two
involved a comprehensive key-word search of SA in social media literature. We used the term
"sentimental AND analysis AND social AND media" as the keywords for our search because it is
a key concept in our review. Results from the database search included 5,031 document results.
Stage three is related to database filtration. A series of inclusion and exclusion filters were
applied to the initial search.
First, we searched only "articles," "conference papers," and "reviews" because these works are
peer-reviewed and graded based on novelty (Kumar et al., 2021). We excluded the research areas
of "Engineering", "Mathematics", "toxicology and pharmaceutics" and "medicine" and keep the
remaining areas, since we realised that many of the published documents on social media were
outside the expected areas, i.e., those related to "business, management," and "computer science."
We chose to include documents published between 2018 and 2022 to eliminate publications that
were not related to the subject matter of the study. This database, after the filtering process, has
523 articles that survived the filtration process and are progressed for bibliometric review, which
we explain in the next section.
4. RESULTS
Table 3 presents the collected information from the Scopus database for 523 publications
published between 2018 and 2022. These articles appeared in 325 scientific journals. Descriptive
statistics and performance analysis of scientific actors (authors, sources, papers) are used for

5
trend analysis. Social media sentiment analysis study is supported by bibliometric data. The total
number of keywords that commonly appear in the article title is 2224, four times the number of
articles. it's noted that 5 years of scientific output were studied and in 2021 we found a huge
surge in publications.
Table 3: Descriptive statistics
Description Results
Timespan 2018:2022
Sources (Journals, Books, etc) 325
Documents 523
Average years from publication 1.76
Average citations per documents 8.189
Average citations per year per doc 2.571
References 25510
DOCUMENT TYPES
article 254
conference paper 252
review 17
DOCUMENT CONTENTS
Keywords Plus (ID) 2224
Author's Keywords (DE) 1499
Authors 1550
Author Appearances 1721
Authors of single-authored documents 44
Authors of multi-authored documents 1506
AUTHORS COLLABORATION
Single-authored documents 45
Documents per Author 0.337
Authors per Document 2.96
Co-Authors per Documents 3.29
Collaboration Index 3.15
4.1. Publication Trend
The distribution of articles by year of publication indicates that sentiment analysis in social media
has gained increasing scholarly interest over the last 20 years (see Fig. 1 below). Most sentiment
analysis in social media research were published in 2021 (n = 789).
Figure 1: Publication trend (1972–2022).

6
The trend topics (see table 4) in this analysis is “sentiment analysis” it appeared (n = 292)
followed by “social networking (online)” (n = 158), while the figure 2 illustrated trend topic areas
of previous studies.
Table 4: The trend topics
Topics freq year_q1 year_med year_q3
sentiment analysis 292 2020 2021 2022
social networking (online) 158 2019 2020 2021
social media 90 2020 2021 2021
classification (of information) 68 2019 2021 2021
deep learning 64 2020 2021 2021
data mining 60 2018 2019 2021
learning systems 57 2019 2020 2021
machine learning 52 2019 2021 2021
learning algorithms 48 2019 2020 2021
natural language processing systems 34 2018 2020 2021
4.2. Other Important Trends and Insights of the Topic Areas
The distribution of scientific production by country in the subject of sentimental analysis and
social media reveals that India has the most scientific production, with 224 documents. Figure 3
shows the number of articles produced by the authors of different countries (SCP) and the rate of
cooperation of each country’s authors with other countries' authors (MCP). For example, Chinese
authors have written 18 articles, and China has co-written 42 articles with other countries.
Subsequently, the authors from India ranked second with 2 articles, and the author’s co-
authorship with other countries is at 30 articles.
Figure 2: Author’s Collaboration Network
We used document analysis to identify emerging trends. The trend topics for abstracts are as
shown in Figure 4. To reveal the conceptual structure of the documents, we present the thematic
map of author keywords as shown in Figure 4. In Figure, the research themes obtained from the
conceptual structure of documents are characterised by two parameters: ‘density’ and ‘centrality’.
The clusters represent the themes of the research, and the size of the clusters is proportional to the

7
number of keywords. The factorial analysis of the documents using correspondence analysis as a
topic dendrogram is shown in figure 5 below. The topic dendrogram shows the clustering of
documents.
Figure 3: The thematic map of author keywords
Following figure 5 demonstrates a topic dendrogram on the topic area.
Figure 4: Factorial Map
The TreeMap (in figure 6) highlights the available keyword combinations, while figure 7 shows
the wordcloud. Using this, we can identify them and use the word cloud to indicate what they
stand for: with” sentimental analysis and social media” are areas of study, in order of magnitude.

8
Figure 5: Word TreeMap
Figure 6: Word Cloud Author's keywords
5. DISCUSSION
The rise of SA research has spurred scholarly research in a variety of fields, from computer
science to the social sciences, especially in the last two years. These are perhaps the two most
notable findings of the bibliometric analysis, which lead us to expect that this field of research
will continue to grow and be complemented by a variety of disciplines in the next few years. In
this paper, we conducted a bibliometric analysis of academic papers from Scopus on the topic of
SA in social media research from 2018 to 2022. The findings presented would provide future
researchers and journal editorial board members with new insights into SA and its potentials,
enhancing their understanding.
The study also found an exponential increase in the number of publications on the topic of SA in
social media research since 2018. The growing profile of publications can be used to predict
changes in the coming years in different countries. In addition, we identified influential articles in
SA in social media research and the contribution to understanding the role of SA in social media
in promoting the advancement of research, which is still needed for future directions.

9
6. CONCLUSION
Bibliometric analysis is a scientific method that can be used by both experienced and developing
researchers to conduct a retrospective of large and rich business study topics. The analysis
provides a comprehensive picture of the research trend and rapid development of SA in social
media research. They can help concerned researchers gain a comprehensive understanding of the
research status, find research assistants, allocate resources, and determine the best relevant
research topics. It is very important to evaluate the quality of such a huge research project. This
paper offered a high-level, intuitive understanding of how SA can be used to interpret new
insights from various contents/posts on social media. This paper can be viewed as a useful
resource for academics and early-career professionals who may have interests in exploring or
adopting sentiment analysis for any social media-oriented research.
The following future research directions are proposed:
 We can use other databases, such as WOS as a substitute and perform it in R packages.
Moreover, the bibliometric analysis proposed in this paper can be extended to databases
other than the SCI /SSCI index and to data collected from different periods.
 We may use other software such as VOSviewer in further study for more précised
outcome. An additional study is needed to investigate this area further.
 The introduction of the extracted data insights is entitled "Sentimental analysis" and
"social media" only, which can be broadened in future study for higher number of sample
collections. At the same time, a future study can be more specific by including other
terms like "customers", "online retailer" and "organization" which have not yet been
analysed within the consumer research domain.
Moreover, more studies can focus on specific countries to study the phenomenon of SA in social
media from a different cultural background or a developing nation's perspective. Our further
research may integrate big data research in other fields such as higher education (Fahd, Miah, and
Ahmed, 2021; Miah, Miah, and Shen, 2020) and government developmental aspect (Miah, 2010)
including healthcare information solution development (Aghdam, Watson, Cliff, and Miah,
2020). Various research areas More research is needed to develop a universal model of sentiment
analysis that can be applied to different types of data transformation, to investigate other possible
social media channels to obtain users' opinions, and to broaden the context in which SA can be
applied in order to make future recommendations.
REFERENCES
[1] Aghdam, A.R., Watson, J., Cliff, C., and Miah, S.J. (2020). “Improving Theoretical Understanding
towards Patient-driven Healthcare Innovation: Online Value Co-Creation Perspective: A Systematic
Review”, Journal of Medical Internet Research, 22(4), e16324, doi:10.2196/16324
[2] Aria, M., & Cuccurullo, C. (2017). “bibliometrix: An R-tool for comprehensive science mapping
analysis”. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
[3] Aromataris, E., & Pearson, A. (2014). The systematic review: An overview. American Journal of
Nursing, 114(3), 53–58. https://doi.org/10.1097/01.NAJ.0000444496.24228.2c
[4] Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). “Scopus as a curated, high-quality
bibliometric data source for academic research in quantitative science studies”. Quantitative Science
Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
[5] Bhullar, G., Khullar, A., Kumar, A., Sharma, A., Pannu, H. S., & Malhi, A. (2022). “Time series
sentiment analysis (SA) of relief operations using social media (SM) platform for efficient resource
management”. International Journal of Disaster Risk Reduction, 75(April), 102979.
https://doi.org/10.1016/j.ijdrr.2022.102979
[6] Calefato, F., Lanubile, F., Maiorano, F., & Novielli, N. (2018). “Sentiment Polarity Detection for

10
Software Development”. Empirical Software Engineering, 23(3), 1352–1382.
https://doi.org/10.1007/s10664-017-9546-9
[7] Chiarello, F., Bonaccorsi, A., & Fantoni, G. (2020). “Technical Sentiment Analysis. Measuring
Advantages and Drawbacks of New Products Using Social Media”. Computers in Industry, 123,
103299. https://doi.org/10.1016/j.compind.2020.103299
[8] Devika, M. D., Sunitha, C., & Ganesh, A. (2016). “Sentiment Analysis: A Comparative Study on
Different Approaches”. Procedia Computer Science, 87, 44–49.
https://doi.org/10.1016/j.procs.2016.05.124
[9] Dhana Laxmi, S., Agarwal, R., & Sinha, A. (2020). DSC-IIT ISM at WNUT-2020 Task 2: Detection
of COVID-19 informative tweets using RoBERTa. June, 409–413.
https://doi.org/10.18653/v1/2020.wnut-1.58
[10] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021a). “How to conduct a
bibliometric analysis: An overview and guidelines”. Journal of Business Research, 133(April), 285–
296. https://doi.org/10.1016/j.jbusres.2021.04.070
[11] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021b). “How to conduct a
bibliometric analysis: An overview and guidelines”. Journal of Business Research, 133(May), 285–
296. https://doi.org/10.1016/j.jbusres.2021.04.070
[12] Fahd, K., Miah, S.J., and Ahmed, K. (2021). “Predicting student performance in a blended learning
environment using learning management system interaction data”, Applied Computing and
Informatics, https://doi.org/10.1108/ACI-06-2021-0150
[13] Gray, A. (2019). “Body as voice: Restorative dance/movement psychotherapy with survivors of
relational trauma”. The Routledge International Handbook of Embodied Perspectives in
Psychotherapy: Approaches from Dance Movement and Body Psychotherapies, 147–160.
https://doi.org/10.4324/9781315159416
[14] Hausmann, A., Toivonen, T., Fink, C., Heikinheimo, V., Kulkarni, R., Tenkanen, H., & Di Minin, E.
(2020). “Understanding sentiment of national park visitors from social media data”, People and
Nature, 2(3), 750–760. https://doi.org/10.1002/pan3.10130
[15] He, L., Yin, T., & Zheng, K. (2022). “They May Not Work! An evaluation of eleven sentiment
analysis tools on seven social media datasets”. Journal of Biomedical Informatics, 132(February).
https://doi.org/10.1016/j.jbi.2022.104142
[16] Jiang, P., Zhou, J., Fan, Y. Van, Klemeš, J. J., Zheng, M., & Varbanov, P. S. (2021). “Data analysis
of resident engagement and sentiments in social media enables better household waste segregation
and recycling”. Journal of Cleaner Production, 319(July).
https://doi.org/10.1016/j.jclepro.2021.128809
[17] Joshi, N., & Itkat, S. (2014). “A Survey on Feature Level Sentiment Analysis”. International Journal
of Computer Science and Information Technologies, 5(4), 5422–5425.
http://www.ijcsit.com/docs/Volume 5/vol5issue04/ijcsit20140504135.pdf
[18] Kumar, A., Beri, T., & Sobti, T. (2021). A Survey of Sentiment Analysis and Opinion Mining.
Springer Singapore. https://doi.org/10.1007/978-981-33-4367-2_39
[19] Kumar, S., Pandey, N., Lim, W. M., Chatterjee, A. N., & Pandey, N. (2021). “What do we know
about transfer pricing? Insights from bibliometric analysis”. Journal of Business Research,
134(March), 275–287. https://doi.org/10.1016/j.jbusres.2021.05.041
[20] Liao, H., Tang, M., Li, Z., & Lev, B. (2019). “Bibliometric analysis for highly cited papers in
operations research and management science from 2008 to 2017 based on Essential Science
Indicators”. Omega (United Kingdom), 88, 223–236. https://doi.org/10.1016/j.omega.2018.11.005
[21] Lohse, B. (2013). “Facebook Is an Effective Strategy to Recruit Low-income Women to Online
Nutrition Education”. Journal of Nutrition Education and Behavior, 45(1), 69–76.
https://doi.org/10.1016/j.jneb.2012.06.006
[22] Mathew, L., & R, B. V. (2020). Techniques for Sentiment Analysis using Pre-trained Models. Iccmc,
340–345.
[23] Medhat, W., Hassan, A., & Korashy, H. (2014). “Sentiment analysis algorithms and applications: A
survey”. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
[24] Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). “Sentiment analysis in hospitality and
tourism: a thematic and methodological review”. International Journal of Contemporary Hospitality
Management, 34(1), 46–77. https://doi.org/10.1108/IJCHM-02-2021-0132
[25] Mejova, Y., Shirsat, V., & Jagdale, R. S. (2009). “Sentiment Analysis: An Overview Hybrid Sent
iment Analysis Framework for a Morphologically Rich Language Jelena Mit rović,” Miljana

11
Mladenovic Subgroup det ect ion in ideological discussions Mona Diab Sent iment Analysis of Event
s from T wit t er Using Op.
[26] Miah, S.J. (2010). “A new semantic knowledge sharing approach for e-government systems”, In the
proceedings of the 4th IEEE International Conference on Digital Ecosystems and Technologies, 13-
16 April 2010, Dubai, United Arab Emirates.
[27] Miah, S.J., Miah, M., and Shen, J. (2020). “Learning Management Systems and Big Data
Technologies for Higher Education”, Education and Info Technologies, 25, 725–730,
https://doi.org/10.1007/s10639-020-10129-z
[28] Obaidi, M., Nagel, L., Specht, A., & Klünder, J. (2022). “Sentiment analysis tools in software
engineering: A systematic mapping study”. Information and Software Technology, 151(November
2021), 107018. https://doi.org/10.1016/j.infsof.2022.107018
[29] Okoli, C., & Schabram, K. (2010). “Working Papers on Information Systems A Guide to Conducting
a Systematic Literature Review of Information Systems Research”. Working Papers on Information
Systems, 10(2010). https://doi.org/10.2139/ssrn.1954824
[30] Oxford Dictionaries. (2013). The Definition Of Social Media.
http://www.oxforddictionaries.com/definition/engli
[31] Pathak, A. R., Pandey, M., & Rautaray, S. (2021). “Topic-level sentiment analysis of social media
data using deep learning”. Applied Soft Computing, 108, 107440.
https://doi.org/10.1016/j.asoc.2021.107440
[32] Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). “Scientific procedures and
rationales for systematic literature reviews (SPAR-4-SLR)”. International Journal of Consumer
Studies, April 2022. https://doi.org/10.1111/ijcs.12695
[33] Prager, J. (2006). “Opinion mining dan sentiment analysis”. In Foundations and Trends in
Information Retrieval (Vol. 1, Issue 2).
[34] Qian, C., Mathur, N., Hidayati, N., & Arora, R. (2022). “Understanding public opinions on social
media for financial sentiment analysis using AI-based techniques”. Information Processing and
Management, 59(6), 103098. https://doi.org/10.1016/j.ipm.2022.103098
[35] Ravi, K., & Ravi, V. (2015). “A survey on opinion mining and sentiment analysis: Tasks, approaches
and applications”. Knowledge-Based Systems, 89, 14–46.
https://doi.org/10.1016/j.knosys.2015.06.015
[36] Tang, M., Liao, H., & Su, S. F. (2018). “A Bibliometric Overview and Visualization of the
International Journal of Fuzzy Systems Between 2007 and 2017”. International Journal of Fuzzy
Systems, 20(5), 1403–1422. https://doi.org/10.1007/s40815-018-0484-5
[37] Vrontis, D., Siachou, E., Sakka, G., Chatterjee, S., Chaudhuri, R., & Ghosh, A. (2022). “Societal
effects of social media in organizations: Reflective points deriving from a systematic literature review
and a bibliometric meta-analysis”. European Management Journal, 40(2), 151–162.
https://doi.org/10.1016/j.emj.2022.01.007
[38] Wan, Q., Xu, X., Zhuang, J., & Pan, B. (2021). “A sentiment analysis-based expert weight
determination method for large-scale group decision-making driven by social media data”. Expert
Systems with Applications, 185(932), 115629. https://doi.org/10.1016/j.eswa.2021.115629
[39] Whittemore, R., Chao, A., Jang, M., Minges, K. E., & Park, C. (2014). “Methods for knowledge
synthesis: An overview”. Heart and Lung: Journal of Acute and Critical Care, 43(5), 453–461.
https://doi.org/10.1016/j.hrtlng.2014.05.014
[40] Xu, Q. A., Chang, V., & Jayne, C. (2022). “A systematic review of social media-based sentiment
analysis: Emerging trends and challenges”. Decision Analytics Journal, 3(June), 100073.
https://doi.org/10.1016/j.dajour.2022.100073
[41] Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). “Supply chain finance: A systematic
literature review and bibliometric analysis”. International Journal of Production Economics,
204(August), 160–173. https://doi.org/10.1016/j.ijpe.2018.08.003
[42] Xu, Z., Ge, Z., Wang, X., & Skare, M. (2021a). “Bibliometric analysis of technology adoption
literature published from 1997 to 2020”. Technological Forecasting and Social Change, 170(May),
120896. https://doi.org/10.1016/j.techfore.2021.120896
[43] Xu, Z., Wang, X., Wang, X., & Skare, M. (2021b). “A comprehensive bibliometric analysis of
entrepreneurship and crisis literature published from 1984 to 2020”. Journal of Business Research,
135(June), 304–318. https://doi.org/10.1016/j.jbusres.2021.06.051
[44] Zanjirchi, S. M., Rezaeian Abrishami, M., & Jalilian, N. (2019). “Four decades of fuzzy sets theory in
operations management: application of life-cycle, bibliometrics and content analysis”. Scientometrics,

12
119(3), 1289–1309. https://doi.org/10.1007/s11192-019-03077-0
[45] Zhang, T., Xu, B., Thung, F., Haryono, S. A., Lo, D., & Jiang, L. (2020). “Sentiment Analysis for
Software Engineering: How Far Can Pre-trained Transformer Models Go?”Proceedings - 2020 IEEE
International Conference on Software Maintenance and Evolution, ICSME 2020, 70–80.
AUTHORS
Zahra Dahish is a PhD student who is working with textual analytics and social media
big data in retailing industry in the Newcastle Business School, University of Newcastle,
Australia.
Shah J Miah is a professor and head of business analytics in Newcastle Business School,
University of Newcastle, Australia. He has extensively published in the areas of big data
analytics and social media research over the past decades.

Exploring Sentiment Analysis Research: A Social Media Data Perspective

More Related Content

Similar to Exploring Sentiment Analysis Research: A Social Media Data Perspective (20)

Recently uploaded (20)

Exploring Sentiment Analysis Research: A Social Media Data Perspective