Sentiment analysis to support business decision-making. A bibliometric study

AIMS Mathematics, 9(2): 4337−4375. DOI: 10.3934/math.2024215 Received date: 10 November 2023 Revised date: 24 December 2023 Accepted date: 27 December 2023 Published date: 17 January 2024 http://www.aimspress.com/journal/Math Review Sentiment analysis to support business decision-making. A bibliometric study J. A. Aguilar-Moreno, P. R. Palos-Sanchez* and R. Pozo-Barajas Department of Financial Economics and Operations, University of Seville, Seville, Spain * Correspondence: Email: [email protected]. Abstract: Customer feedback on online platforms is an unstructured database of growing importance for organizations, which, together with the rise of Natural Language Processing algorithms, is increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with the intention of understanding the prevailing state of research about the adoption of sentiment analysis methods in organizations when making decisions. It is also a goal to comprehend which business sectors, and areas within the company, they are most applied, and to identify what future challenges that in this area may arise, as well as the main topics, authors, articles, countries, and universities most influential in the scientific literature. To this end, a total of 101 articles have been gathered from the Scopus and Clarivate Analytics Web of Science (WoS) databases, of which 85 were selected for analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.). Sentiment analysis enhances decision-making and promotes customer-centric approaches. Keywords: social media; big data; business decision-making; data mining; opinion mining; sentiment mining; bibliometric analysis; multicriteria decision-making Mathematics Subject Classification: 68, 90, 91 1. Introduction In recent years, unstructured information scattered on the web has become an increasingly relevant source of information to support organizations in decision-making [1]. From the analysis of this data, managers try to improve the relationship with their potential customers and implement 4338 AIMS Mathematics Volume 9, Issue 2, 4337–4375. business improvements [2] in a more affordable, faster, and more direct way than the traditional manner, in which customer experience used to be counted through customer service surveys [3,4]. According to [5], business is the practice of making one’s living or making money by producing or buying and selling products (such as goods and services). More recently, [6] added that “any activity or enterprise entered into for profit”. To this definition we must add the concept of decision-making in the organizational structure of the company, where various authors emphasize the role of business processes or business processes management (BPM) [7]. The definition of business decision-making in a BPM environment is more complex, as according to [8] it depends on the context and the topic of discussion in which the processes take place. Due to the large amount of existing information, this can be incomprehensible for manual analysis both for customers [9] and for organizations that intend to review opinions about their own or competing products or services [10], so the need arises to automate the collection and analysis process [11]. This can be done with sentiment analysis (SA), defined as the field of study that analyzes people’s opinions, feelings, attitudes, and emotions toward entities such as products, services, events, organizations, individuals, problems, or other topics or the attributes expressed about these in a text [12]. This can automate the task of processing large amounts of unstructured data by applying artificial intelligence and machine learning algorithms to obtain relevant information about the opinions, emotions, and attitudes manifested in texts, such as social media posts, product reviews, or blog comments. This method of analysis has become an indispensable tool for organizations that want to know the opinions and needs of their customers in real time, allowing them to make more accurate decisions and enhance their products and services, reformulate processes, take into account the customer, predict trends and market dynamics [13,14], or identify potential risks and business opportunities [15]. Although SA has been studied in a variety of fields, especially in engineering, where there is a considerable number of publications related to precision comparisons between classifier algorithms in the branch of machine learning (ML) of SA, there are still areas of research in which literature reviews or bibliometric analyses are not found that show how research is currently carried out in terms of its practical application in business decision-making. In this context, this article aims to analyze the importance of SA in business decision-making, understand the state of research in relation to this method of analysis applied to decision-making, in which areas it has been most frequently applied, and discover its evolution and current trends in its application through a bibliometric analysis of the existing literature from 2013 until the date of this paper. Thus, the following research questions are proposed: • RQ1: What is the current state of research on sentiment analysis applied to business decisionmaking? • RQ2: What are the areas of business where there is a greater adoption of sentiment analysis methodologies to support decision-making? • RQ3: Which online platforms are most frequently used to obtain customer feedback? • RQ4: How is the application evolving and with what other methods is sentiment analysis combined? The findings indicate that SA methods are becoming increasingly popular [16], and that they are being used in combination with other methodologies such as the multicriteria decision making method (MCDM) and predictive algorithms, among others. The structure of this article is divided into this introductory section in which everything developed in the article is discussed in general terms and the research questions are set out. The second section is the methodology section, which defines in detail the procedure followed, the databases consulted, and their search strings, and the software used for the study. This is followed by the conceptual section, which explains what SA consists of, and the object of study in this work. Next is the results section, in which the results of the research on bibliometrix are shown and commented on, and, in the document 4339 AIMS Mathematics Volume 9, Issue 2, 4337–4375. analysis subsection, a review of the most relevant articles of the compilation is carried out. The discussion section summarizes the findings by answering the research questions posed before completing the article with the last section, the conclusions section. 2. Related works 2.1. Sentiment analysis definition The concept of SA or opinion mining has been defined in the scientific literature by different authors. In Table 1 “Definitions of sentiment analysis” some of these definitions are collected: Table 1. Definitions of sentiment analysis. Author(s) Definition Liu [12] Sentiment analysis is an area of research that is dedicated to analyzing the opinions, attitudes, valuations, and emotions that people have toward different entities and their characteristics, which are expressed in written texts. This field of study, also known as opinion mining, focuses on entities like products, services, organizations, individuals, events, concerns, or subjects. Bhadane et al. [17] Sentiment analysis is a discipline that is part of natural language processing, and which is responsible for recognizing the opinion expressed on elements with subjective information present in a text. Batista et al. [18] The automatic identification of opinions or emotions about products, services, or experiences from user-generated content in digital media is the main objective of sentiment analysis, also known as opinion mining. Esuli and Sebastiani [19] Opinion mining (or sentiment analysis, also referred to as sentiment classification) is an emerging subdiscipline that lies between information retrieval and computational linguistics. Its main focus, rather than identifying the theme of the text, is to identify the opinion expressed in it. Taboada et al. [20] The extraction of subjectivity and polarity from a text (or also from an oral discourse) is what is known as sentiment analysis. Pang and Lee [21] Opinion mining, alternatively referred to as sentiment analysis, centers on the computational handling of opinions, subjectivity, and emotions expressed within texts. Serrano-Guerrero et al. [22] Opinion mining, also called sentiment analysis, is the research area in charge of analyzing user opinions to exploit their information. Of these, the definition given by Liu [12] is one of the most complete and recent, and has led to it being considered and included in numerous articles related to SA in their literature review sections. In turn, SA is included within the research field of natural language processing (NLP). This field of research refers to the techniques with which machines process the language of people [23] in order to generate some utility, and includes different techniques such as speech recognition and speech generation, spelling and grammar correction, translation, intelligent writing assistants, named entities identification, analysis of topics, or the one on which this paper focuses, SA [24,25]. NLP is one of the branches framed in the field of artificial intelligence, a discipline that, as shown in Figure 1 “Taxonomy of the subareas of artificial intelligence with emphasis on SA”, has as main branches 4340 AIMS Mathematics Volume 9, Issue 2, 4337–4375. machine learning, computer vision, knowledge-based systems, fuzzy logic, natural language processing, robotics, automated planning and scheduling, expert systems, and optimization. [24,26–28]. Figure 1. Taxonomy of the subareas of artificial intelligence with emphasis on SA. Within the field of SA there are several techniques to perform the analysis. Figure 1 “Taxonomy of the subareas of artificial intelligence with emphasis on SA” shows that within SA there are three types of methods [29,30]: -Machine learning based: From a set of labeled data, the classifier is trained to detect patterns in the sample, new unlabeled data is then added, and the classifier will determine the sentiment of these. The best-known and most accurate classifying algorithms are support vector machine (SVM), Naive Bayes, and decision trees [31]. -Lexicon-based: The feeling of the sentences is determined by coincidence with the words included in a predetermined dictionary in which each word is assigned a positive, negative or neutral connotation expressed in polarity values between -1 and +1, -1 being the most negative value, 0 the neutral value, and +1 the most positive value. Some of the best-known lexicons are VADER [32] and SentiWordNet [19]. -Hybrid: This is a mixture of the two previous methods. There are also three levels of classification according to the level of detail of the analysis [16,33]: -Document-level: Classifies the sentiment of a document in general. -Sentence-level: Classifies the feeling of all the different sentences that make up a text. Sentences can be considered short documents, so this level is similar to the document level; however, it involves more difficulty due to less information being contained in the sentences regarding the documents. -Aspect-level or aspect based sentiment analysis (ABSM): Identifies entities and associates the 4341 AIMS Mathematics Volume 9, Issue 2, 4337–4375. detected sentiment with each of the detected entities. As explained in the conclusions of this article, it is one of the most widely used levels when detecting sentiment in reviews, as it allows us to know which attributes are the best or worst for a product or service. The purpose of applying SA methods in business is generally for monitoring online opinions of customers, experts, and other influential people that represent a source of unstructured data that is infeasible to parse manually and which serves as a rich source of information to improve the quality of customer service, make improvements in the marketed product, and prevent future emergencies. It should be noted that, although SA can be a boost to business, it currently has great limitations. All the same, its combination with other methods from other research fields such as psychology, engineering, or marketing, resulting in hybrid models, could improve its application in the future [34]. Among the main limitations of SA are [30]: -Lack of resources: Algorithms require a large annotated dataset and manual labeling requires considerable time. In addition, most of the research is focused on English, and with other languages the accuracy is not as high. -Web jargon: Some publications use a very specific vocabulary, which is another limitation for sentiment dictionaries. -Irony and sarcasm: Reviews expressed in an ironic tone may contain a word that denotes a sentiment but says the opposite. -Presence of multiple aspects in the same sentence: It may happen that in the same sentence one aspect is commented positively and another negatively, which leads to greater difficulty in classifying the feeling of the sentence. 2.2. Systematic literature reviews about sentiment analysis Before searching the articles in the databases, a compilation of previous articles from systematic literature reviews and related bibliometric analyses was conducted with the aim of comprehending from what perspectives the relationship between SA and business had been studied so far and to know how they had formulated the search chain. These previous works carried out research adopting an approach focused on methodological or technical aspects of SA, so it was determined that there were no literature reviews, nor bibliometric analyses focused on the practical aspects of how SA can be used to optimize and improve decision-making in a company. Table 2 “SLR and previous bibliometrics” shows the systematic literature reviews and bibliometric analyses that were considered: 4342 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Table 2. SLR and previous bibliometrics. Journal JCR Title Author(s) Year Type/Perio d Data sources Context Screened works/prim ary studies Query JOURNAL OF INFORMATI ON SCIENCE Q3 Bibliometrics of sentiment analysis literature Keramat f-ar and Amirkh a-ni [35] 2019 Biblio m. 2003- 2016 WoS Bibliometric analysis of sentiment analysis literature for the quantitative and qualitative evaluation of current research trends. This involves analyzing subject categories, languages, author impact, publication venues and geographic distribution. Identifying trends in keywords and potential areas for future research will also be explored. 3225 "Opinion mining", "sentiment analysis" CONCURRE NCY AND COMPUTATI ONPRACTICE & EXPERIENC E Q3 Big data and sentiment analysis: A comprehensiv e and systematic literature review Hajiali [36] 2020 SLR. 2005- 2019 Google Scholar, Springer link, Emerald, IEEE, Science Direct, WoS, ABI/Inform Global ProQuest The purpose of this study is to present a thorough and detailed analysis of the latest big data and sentiment analysis techniques, providing valuable insights into this field. The study presents an all-encompassing review of the chosen articles, highlighting their key characteristics and comparing their findings. Additionally, it identifies the key challenges, and it highlights the limitations of existing techniques and provides recommendations for future research to tackle these challenges. 23/15351 "Big Data Analysis", "Sentiment Analysis", "Big Data and Sentiment Analysis" ELECTRONI C MARKETS Q2 Artificial intelligence in E-Commerce: a bibliometric study and literature review Bawack et al. [37] 2022 SLR & Biblio m. 1975- 2020 WoS This paper summarizes AI research in e-commerce and suggests ways for Information Systems (IS) research to contribute, with sentiment analysis, trust, personalization, and optimization as core themes. 229 (SLR) & 4335 (Bibliom.) /4414 "Electronic Commerce", "Artificial intelligence", "data science", "fuzzy logic", "machine learning", "natural language processing", "text mining", etc. JOURNAL OF HOSPITALIT Y AND TOURISM TECHNOLO GY Q2 Social media analytics in hospitality and tourism: A systematic literature review and future trends Mirzaali a-n and Halpenn y [38] 2019 SLR. 2010- 2018 SAGE, EBSCOhost , Google Scholar, Science Direct, Scopus The objective of this work is to examine the utilization of social media analytics in the field of hospitality and tourism studies for the analysis of big data derived from social media platforms. Additionally, it aims to propose enhanced methodologies based on previous and current practices. 85/1973 "SMA", "opinion mining", "sentiment analysis", "text analysis", "predictive analytics", "topic modeling", "spatial analysis", "comparative analysis" Continued on next page 4343 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Journal JCR Title Author(s) Year Type/Peri od Data sources Context Screened works/pri mary studies Query FIFTH INFORMATIO N SYSTEMS INTERNATION AL CONFERENCE N/A Sentiment Analysis in Social Media and Its Application: Systematic Literature Review Drus and Khalid [39] 2019 SLR. 2014- 2019 ACM, Emerald Insight, IEEE Xplore, Science Direct, Scopus This article presents a comprehensive report on the review of Sentiment Analysis in social media, encompassing the exploration of methods employed, the social media platforms utilized, and their respective applications. 24/407 "Sentiment analysis", "social media", "Facebook", "Twitter" IEEE-INST ELECTRICAL ELECTRONI CS ENGINEERS INC Q2 Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges Shayaa et al. [40] 2018 SLR. 2001- 2016 WoS This paper presents a SLR on Sentiment Analysis and Opinion Mining, covering both technical and nontechnical aspects. It examines the different techniques and types of OM or SA, as well as its application areas. 58/365 "Opinion mining", "sentiment analysis" CONCURRE NCY AND COMPUTATI ONPRACTICE & EXPERIENC E Q3 Systematic literature review of sentiment analysis on Twitter using soft computing techniques Kumar and Jaiswal [41] 2019 SLR. 2000- 2018 ACM, IEEE, Elsevier, Wiley, Springer This study examines the use of soft computing techniques, which are based on AI, to analyze sentiment on Twitter. The goal is to identify areas for further research and potential future applications of these techniques. 60/502 "Twitter", "sentiment analysis", "opinion mining", "soft computing techniques", "machine learning", "supervised methods", etc. Procedia computer science N/A A Bibliometric Analysis of COVID-19 Vaccines and Sentiment Analysis Sarirete [42] 2021 Bibliom . 2020- 2021 Scopus Conducts a bibliometric study of articles studying the acceptance of the COVID-19 vaccine through SA. The results show that knowing the state of the art is crucial for health agencies to help prevent misinformation and improve vaccine uptake. 1237 "COVID-19", "vaccin", "sentiment analysis", "social media", "opinion mining", "text mining", "Twitter", "social media analytics", "Big data", "machine learning", "sentiment classification", "natural language processing" Continued on next page 4344 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Journal JCR Title Author(s) Year Type/Peri od Data sources Context Screened works/pri mary studies Query COMPUTER SCIENCE REVIEW Q1 A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews Jain et al. [43] 2021 SLR. 2017- 2020 Google Scholar The purpose is to investigate and assess the application of ML in SA of online reviews within the hospitality and the tourism industry. 68/253 "Machine learning", "consumer sentiment analysis", "consumer views", "recommendation prediction", "fake reviews", "decision tree", "support vector machine", "consumer intention", "neural network" JOURNAL OF INFORMATIO N SCIENCE Q3 A comprehensive bibliometric analysis of opinion mining and sentiment analysis global research output Musa et al. [44] 2022 Biblio m. 2000- 2020 Scopus This research investigates the worldwide research output on OM and SA between 2000 and 2020. Bibliometric metrics were employed to evaluate trends, highly cited papers, prolific authors, countries, leading institutions, funding agencies, and research subject areas, with the help of the Bibliometrix package in R. 7603/87 13 "Opinion mining", "sentiment analysis" GOVERNMEN T INFORMATIO N QUARTERLY Q1 Sentiment analysis of public services for smart society: Literature review and future research directions Verma [45] 2022 Biblio m. 2010- 2021 Scopus SA can be used to build a smart society, understanding the trends, performance, and content of SA applications. This review reveals the usefulness of SA in promoting citizen engagement for addressing social issues such as crime prediction, disaster management and traffic congestion. 353/845 N/A COMPUTER SCIENCE REVIEW Q1 The evolution of sentiment analysis-A review of research topics, venues, and top cited papers Mantyla et al. [46] 2018 SLR & Bibliom . 2000- 2016 Scopus Over the years, sentiment analysis (SA) has moved beyond analyzing e-commerce customer feedback to analyzing reviews from platforms like Twitter and Facebook. As a result, the use of SA has expanded beyond product reviews to topics such as medicine, stock markets, disasters, software engineering, elections and cyberbullying. 6996 "Sentiment analysis", "Opinion mining", "Sentiment classification", "Opinion analysis", "Semantic orientation", "Sentiwordnet", "Opinion classification", "Sentiment mining", "Subjectivity analysis", "Sentic", "Subjectivity classification" Continued on next page 4345 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Journal JCR Title Author(s) Year Type/Peri od Data sources Context Screened works/pri mary studies Query KNOWLEDG E-BASED SYSTEMS Q1 Fuzzy logic applied to opinion mining: A review Serrano- Guerrer o et al. [47] SLR 2010- 2020 WoS and Scopus The review portrays different uses of fuzzy logic and summarizes over 120 articles published in the past decade regarding tasks and applications of opinion mining. 120/2000 ‘‘Fuzzy logic’’, ‘‘fuzzy sets’’ ,‘‘interval-valued fuzzy sets’’ (IVFSs), ‘‘pythagorean fuzzy sets’’ (PFSs), ‘‘type-2 fuzzy sets’’, (T2FSs), ‘‘interval type-2 fuzzy sets’’ (IT2FSs), ‘‘neutrosophic fuzzy sets’’ (NFSs), ‘‘hesitant fuzzy sets’’ (HFSs), and so on), ‘‘choquet integral, ‘‘sentiment analysis’’, ‘‘hate’’, ‘‘affective computing’’, ‘‘opinionmining’’,‘‘microblo gging’’, ‘‘subjectivity’’, ‘‘polarity’’, ‘‘emotion’’, ‘‘feelings’’, ‘‘irony’’,‘‘sarcasm’’,‘‘cyberb ullying’’, ‘‘twitter’’, ‘‘rating detection’’, ‘‘ewom’’ (electronic word-of-mouth), ‘‘aspect detection’’, ‘‘feature detection’’, ‘‘review-based’’, ‘‘opinion-based’’ and ‘‘aspect-based’’ 4352 AIMS Mathematics Volume 9, Issue 2, 4337–4375. 4.3. Author analysis 3.9% of the 256 authors mentioned in the general information section, that is 10 authors according to Table 8 “Ratio of publications per author” (Amankwah-Amoah, Herrera-Viedma, Kalpana, Lau, Li J, Morente-Molinera, Sheng J, Wang X, and Yang Z.), published 2 articles. Only one author (Li Y), published more than 2 articles, as shown in Table 7 “Number of documents per author”. 95.7% of the authors wrote only 1 article. Table 7. Number of documents per author. Authors Articles Articles Fractionalized LI Y 3 0.83 AMANKWAH-AMOAH J 2 0.67 HERRERA-VIEDMA E 2 0.45 KALPANA P 2 1.00 LAU R 2 0.50 LI J 2 0.50 LUO AND 2 0.70 MORENTE-MOLINERA J 2 0.53 SHENG J 2 0.67 WANG X 2 0.67 YANG Z 2 0.45 ABIRAMI A 1 0.50 Table 8. Ratio of publications per author. Documents written N. of Authors Proportion of Authors 1 245 0. 957 2 10 0. 039 3 1 0. 004 The following Table 9 “Impact of authors” shows the impact index of authors in the column “h_index”. In this case, the number of publications is proportional to the author’s impact because the number of citations is quite homogeneous among all the articles, with no author having achieved a greater impact with a single article than the authors who have published a greater number of articles. In this way, the authors with the greatest impact coincide with those who have produced the most publications, Li Y being the first of them with an h index of 3, followed by those with an h index of 2: Amankwah-Amoah, Herrera-Viedma, Kalpana, Lau, Li J, Morente-Molinera, Sheng, and Wang. 4353 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Table 9. Impact of authors. Element h_index g_index m_index TC NP PY_start LI Y 3 3 0. 333 34 3 2015 AMANKWAH-AMOAH J 2 2 0. 286 139 2 2017 HERRERA-VIEDMA E 2 2 0. 5 27 2 2020 KALPANA P 2 2 1 6 2 2022 LAU R 2 2 0. 222 34 2 2015 LI J 2 2 0. 286 41 2 2017 MORENTE-MOLINERA J 2 2 0. 4 27 2 2019 SHENG J 2 2 0. 286 139 2 2017 WANG X 2 2 0. 286 139 2 2017 ABIRAMI A 1 1 0. 143 27 1 2017 Reviewing the publications that each of the previous authors have in their respective academic profiles, it is observed that Wang and Sheng are the authors who have placed the most emphasis in recent years on research into OM, online reviews, and Big Data, being co-authors in most of the articles. There is also Morente-Molinera, a researcher at the University of Granada who has focused his research on topics such as customer satisfaction, SA, decision-making with multicriteria methods, and the Internet of Things (IoT), and who also has worked as a co-author with Herrera-Viedma, professor at the University of Granada, specializing in the fields of Computer Science and Artificial Intelligence. Li J has published papers on online customer reviews to improve competitive intelligence, review writing style identification, and perform SA in Chinese. The other authors included in the previous table have used SA as a methodology not recurrently, but specifically, in their publications. The following Figure 6 “Collaboration networks between authors” shows the collaboration groups, highlighting the Granada group of Herrera-Viedma and Morente-Molinera and the group of Wang and Sheng. Figure 6. Collaboration networks between authors. 4354 AIMS Mathematics Volume 9, Issue 2, 4337–4375. To obtain an overview, the nationality of the authors was consulted in order to understand which countries are the most prolific countries for this field of study. Table 10 “Number of publications by country” below shows that China stands out from the rest with 18 publications, representing 21.2% of the total number (85 articles). There are 14 publications of which the country could not be identified. India emerges as the country with the second-highest publication count with 10 (11.8%), followed by the USA with 9 publications (10.6%) and Spain with 9 (9.4%). Table 10. Number of publications by country. Country Articles SCP MCP Freq MCP_Ratio CHINA 18 15 3 0. 212 0. 167 N/A 14 14 0 0. 165 0 INDIA 10 10 0 0. 118 0 USA 9 7 2 0. 106 0. 222 SPAIN 8 6 2 0. 094 0. 25 KOREA 4 3 1 0. 047 0. 25 GERMANY 3 3 0 0. 035 0 ITALY 3 3 0 0. 035 0 UNITED KINGDOM 3 3 0 0. 035 0 PAKISTAN 2 1 1 0. 024 0. 5 BRAZIL 1 0 1 0. 012 1 FINLAND 1 1 0 0. 012 0 FRANCE 1 0 1 0. 012 1 GREECE 1 1 0 0. 012 0 INDONESIA 1 1 0 0. 012 0 MOROCCO 1 1 0 0. 012 0 PORTUGAL 1 1 0 0. 012 0 SAUDI ARABIA 1 1 0 0. 012 0 SLOVAKIA 1 1 0 0. 012 0 TUNISIA 1 0 1 0. 012 1 TURKEY 1 1 0 0. 012 0 These data are graphically represented in the choropleth map of Figure 7 “Choropleth map of production by countries and collaboration between countries” in which international collaborations are also represented. The USA with China, China with India, and India with the UK are the only international collaborations for which more than one article has been recorded, although there are 15 other pairs of international collaborations that have recorded only one publication: China-Australia, China-Iran, China-Netherlands, China- UK, India-France, India-Netherlands, Korea-France, Pakistan- Saudi Arabia, Spain-Brazil, Spain-Finland, Tunisia-Saudi Arabia, UK-Brazil, UK-France, UK- Netherlands, and USA-Iran. 4355 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Figure 7. Choropleth map of production by countries and collaboration between countries. In terms of the number of citations, the four countries with the highest impact are Spain, which leads the ranking with 366, followed by China, the USA, and Pakistan with 298, 274, and 246 publications, respectively. The best average number of citations per article is Pakistan with 123 citations. Table 11, titled “Impact by country”, showcases the ranking of the top 10 nations based on their citation counts. Table 11. Impact by country. Country TC Average Article Citations SPAIN 366 45. 75 CHINA 298 16. 56 USA 274 30. 44 PAKISTAN 246 123. 00 ITALY 181 60. 33 UNITED KINGDOM 141 47. 00 KOREA 44 11. 00 PORTUGAL 26 26. 00 INDIA 25 2. 50 GERMANY 17 5. 67 In order to identify the research clusters with a higher level of concreteness, the universities with more publications are checked based on the affiliation of the authors. In Table 12 “Publications by affiliation”, National Cheng Kung University (Taiwan) and Northeastern University (USA) hold the distinction of being the universities with the highest publication count, each contributing 5 articles. Guangdong University of Foreign Studies (China), Indian Institutes of Information Technology (India), Renmin University of China (China), and the University of Granada (Spain) are ranked next with 4 papers. 4356 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Table 12. Publications by affiliation. Affiliation Articles NATL CHENG KUNG UNIV 5 NORTHEASTERN UNIV 5 GUANGDONG UNIV FOREIGN STUDIES 4 INDIAN INST INFORMAT TECHNOL 4 RENMIN UNIV CHINA 4 UNIV GRANADA 4 INDIAN INST INFORMAT TECHNOL DESIGN AND MFG 3 UNIV BRISTOL 3 UNIV KENT 3 UNIV LISBON 3 4.4. Document analysis 4.4.1. Research areas analysis In order to find out which research areas are the most active in the collection of publications, a count of the number of articles per year and research area has been made. Each article may have several research areas that overlap each other, so the count of articles by research areas does not have to coincide with the total number of articles. Figure 8 “Research areas analysis” shows that Computer Science, Business & Economics, and Engineering are the most represented research areas, increasing in frequency, especially since 2019. Figure 8. Research areas analysis. 4357 AIMS Mathematics Volume 9, Issue 2, 4337–4375. 4.4.2. Citation analysis Five articles exceed one hundred citations. Reyes et al. [63] stands out with a noteworthy citation count of 298, averaging 27.09 TC per year, followed by Khan et al. [64] with 245 citations and an average of 24.50 per year. Del Vecchio et al. [65] obtained 143 citations and an average of 23.83. Then there is Sheng et al. [66] with 109 citations and an average of 15.57 per year, and Chang et al. [67] with 106 citations and an average of 21.20 per year. Table 13 “Most cited papers” exhibits the 25 articles with the greatest citation counts. Table 13. Most cited papers. Paper DOI Total Citations TC per Year Normalized TC KINGS A, 2013, LANG RESOUR EVAL 10.1007/s10579-012- 9196-x 298 27. 09 2. 74 KHAN FH, 2014, DECIS SUPPORT SYST 10.1016/j.dss.2013.09. 004 245 24. 50 1. 00 DEL VECCHIO P, 2018, INF PROCESS MANAGE 10.1016/j.ipm.2017.10 .006 143 23. 83 3. 75 SHENG J, 2017, INT J PROD ECON 10.1016/j.ijpe.2017.06 .006 109 15. 57 3. 50 CHANG YC, 2019, INT J INF MANAGE 10.1016/j.ijinfomgt.20 17.11.001 106 21. 20 6. 49 SCHUMAKER RP, 2016, DECIS SUPPORT SYST 10.1016/j.dss.2016.05. 010 65 8. 13 2. 30 CHATTERJEE S, 2019, DECIS SUPPORT SYST 10.1016/j.dss.2019.02. 008 56 11. 20 3. 43 CHANG WL, 2018, ELECT COMMER RES APPL 10.1016/j.elerap.2018. 01.014 55 9. 17 1. 44 LUO Y, 2021, INT J HOSP MANAG 10.1016/j.ijhm.2020.1 02849 50 16. 67 5. 11 TAO D, 2020, COMPR REV FOOD SCI FOOD SAF 10.1111/1541- 4337.12540 48 12. 00 4. 31 KIM Y, 2016, ONLINE INFO REV 10.1108/OIR-03- 2015-0068 42 5. 25 1. 49 CHUNG W, 2020, INF MANAGE 10.1016/j.im.2018.09. 008 39 9. 75 3. 51 ZHOU F, 2019, IND MANAGE DATA SYST 10.1108/IMDS-01- 2019-0034 30 6. 00 1. 84 SHENG J, 2019, TECHNOL FORECAST SOC CHANG 10.1016/j.techfore.201 8.06.009 30 6. 00 1. 84 KAUFFMANN E, 2019, SUSTAINABILITY 10.3390/SU11154235 29 5. 80 1. 78 YUAN H, 2018, ANN OPER RES 10.1007/s10479-017- 2421-7 29 4. 83 0. 76 ABIRAMI AM, 2017, ONLINE INFO REV 10.1108/OIR-08- 2015-0289 27 3. 86 0. 87 Continued on next page 4358 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Paper DOI Total Citations TC per Year Normalized TC WANG D, 2017, ELECTRON COMMER RES 10.1007/s10660-016- 9233-8 26 3. 71 0. 83 SANTOS CL, 2018, INT J EDUC MANAG 10.1108/IJEM-01- 2017-0027 26 4. 33 0. 68 TROISI O, 2018, INTERN J ENG BUS MANAGE 10.1177/18479790187 67776 25 4. 17 0. 66 PAI MY, 2013, EXPERT SYST APPL 10.1016/j.eswa.2012.1 0.024 24 2. 18 0. 22 MORENTE-MOLINERA JA, 2020, KNOWLEDGE-BASED SYST 10.1016/j.knosys.2020 .105657 23 5. 75 2. 07 FAN ZP, 2018, KYBERNETES 10.1108/K-03-2017- 0116 18 3. 00 0. 47 YANG Z, 2019, IEEE ACCESS 10.1109/ACCESS.201 9.2921403 16 3. 20 0. 98 LIANG D, 2020, FUZZY OPTIM DECIS MAK 10.1007/s10700-020- 09327-8 15 3. 75 1. 35 In the most cited article of the collection, Reyes et al. [63] presents a multidimensional approach based on the characteristics of representativeness and relevance to detect irony in tweets, one of the main challenges faced by SA when determining the polarity of tweets [68] to evaluate a company’s reputation or support decision-making in it. The tweet irony detection model is trained on a corpus of tweets that explicitly claim to be ironic by including the hashtag #irony. The model is evaluated with another corpus of untagged tweets to determine whether they are ironic and checks the results manually; however, this did not obtain a high precision. The second article with the highest number of citations corresponds to Khan et al. [64]. They also use the social network Twitter in their article as a source of information and focus on developing a hybrid classification scheme with several steps of preprocessing the data before being classified to solve the most common problems of SA: classification accuracy, data sparsity, and sarcasm. In preprocessing they clean the corpus by removing URLs, hashtags, usernames, special characters, and stopwords. Once the cleaning is done, they proceed to classify the tweets with the hybrid scheme, which is based, on the one hand, on SA founded on lexicon with the SentiWordNet dictionary and, on the other hand, on the detection and interpretation of emoticons and an enhanced polarity classifier incorporating a lexicon of positive and negative words. The third article of the classification is Del Vecchio et al.’s [65], who applies SA using ML to identify tourists’ opinions of a tourist destination from official hashtags on the main social networks (Facebook, Twitter, and Instagram) to support decision-making regarding the creation of new business models, custom offers, and generating greater trust and transparency for customers. The article aims to demonstrate how tourist-generated content, which makes up a source of data of great value and enormous magnitude in terms of its volume, can be used to create value in a smart tourist destination. Chang et al. [67], the article ranking fifth in terms of the highest citation count, has a similar purpose by extracting ratings and opinions from the TripAdvisor portal about a hotel chain. The particularity of this work is that they perform ABSA, one of the methods that, as already commented 4359 AIMS Mathematics Volume 9, Issue 2, 4337–4375. on in the literature review section, is most frequent when SA is applied to identify opinions about products or services, allowing discriminating between in the same sentence positive and negative attributes about different hotel services such as cleaning, food, and attention, among others. The fourth article on the list corresponds to a literature review that addresses academic works related to Big Data in management research. This work compiles and classifies articles on big data in 5 areas: organization, operation and production, marketing, information management, and others. Within the field of marketing, it incorporates SA. Although this article by Sheng et al. [66] frames SA exclusively from a marketing perspective because it is the business area that is most frequently used, this paper has shown that its application is not only limited to the marketing area, but can be applied to many others, especially operations to detect risks, for example in the supply chain. 4.4.3. Word cloud To identify recurring themes among all the collected works, a word cloud has been generated with the most frequent terms. For greater specificity and significance, it has been decided to base it on bigrams (pairs of words) that are only included in the titles of the articles. The following Figure 9 “Most frequent bigrams cloud” shows the result. Figure 9. Most frequent bigram cloud. Obviously, “social media”, “opinion mining”, “media analytics”, “online reviews”, “business 4360 AIMS Mathematics Volume 9, Issue 2, 4337–4375. intelligence”, and bigrams of similar characteristics are common to most of the articles. There are also some SA methodologies such as “aspect based sentiment”, which focuses on identifying different attributes with their corresponding polarity values within the same sentence instead of assigning a general polarity to that sentence [69], “neural network”, “machine learning”, “clustering algorithm”, “fake reviews”, or “based sarcasm”, which as mentioned above try to produce advances in aspects with which SA encounters certain problems. But, going into more detail there are some specific fields of application of these methods to business: “chain risk”, “supply chain”, “stock market”, “product ranking”, “banking sector”, “cryptocurrency market”, “textual aftersales”, and “corporate reputation”. These bigrams indicate that SA applied to business has already been used to analyze opinions in the supply chain and its risks, to analyze the customer’s view of the company and after-sales service and, to a large extent, in the traditional financial sector both in terms of banking entities and stock markets and in the most disruptive aspect with the rise of cryptocurrencies. Likewise, terms referring to the impact of the COVID-19 pandemic, which greatly affected businesses, are also identified: “covid pandemic”, “covid outbreak”. These keywords already leave some brushstrokes of the topics that have been worked on in the field of SA research applied to business decision-making, providing a general impression about what has been written so far. 4.4.4. Factor analysis Focusing on the content of the collected articles, we proceed to the factor analysis of the documents with the method of multiple correspondence analysis to find out how closely related the keywords of the articles are to each other and to be able to interpret how close or distant are the specific research subareas framed within the application of SA to business. In the following Figure 10 “Factor analysis”, it is observed that the first dimension, located on the abscissa axis is represented by words with more quantitative or technical attributes the greater the degree of the dimension, noting in its highest values keywords that refer to the methods and algorithms “learning algorithms”, “learning systems”, “natural language processing”, etc., and in their values closest to zero or even negative words that have a more subjective or qualitative connotation and that correspond to the application of algorithms to concrete realities “Word of mouth”, “customer”, “opinion”, “investor sentiment”, “twitter”, etc. The second dimension, that of the ordinate axis, refers to the specific areas to which the commented algorithms have been applied, placing in the values below zero the areas focused mainly on the lexicon (articles that develop SA lexicons in specific languages or areas could be included here) and as the “Y” axis increases in value, areas of application are seen in a more business contexts: “investor sentiment”, “customer”, “product”, “online reviews”, “business”, even establishing a separate category due to the large number of articles in this area that is the sector of hospitality and tourism “tourism”. The highest proportion of words is concentrated is in the qualitative area, but they are found in large numbers both in the part most closely linked to the business context and in the most generic part. 4361 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Figure 10. Factor analysis. Considering the two previous dimensions, the following Figure 11 “Most relevant documents of factor analysis” shows the documents that have contributed most to these dimensions. Thus, in the upper left quadrant (application in business context and with subjective or qualitative connotation) are framed the papers of Marcolin et al. [70] and Bacik et al. [71], which deal with hotel guests’ SA; Yalcinkaya et al.’s [72], which analyzes sentiment in restaurants; and Caviggioli et al.’s [73], which analyzes the corporate reputation of companies that adopt Bitcoin as a means of payment. In the upper right quadrant (application in business context, but with a more technical approach) is that of Kim et al. [74], based on an example of comparing smartphones but with the focus on revealing gaps between the market leader and competitors, and valid for any other sector. In the lower left quadrant (articles not so focused on a business context but on the generation of models applicable to the company) is framed the article by Zhang et al. [9] developing a classification model of products that may interest a specific customer according to their preferences based on ABSA reviews of other customers, and the article by Chung and Zeng [75] that develops a model to dissect user emotion and influence in social networks. In the lower left quadrant (in which the articles with a more technical purpose are located) is the article by Dai et al. [76], based on the development of a text mining model to collect and analyze competitive intelligence. 4368 AIMS Mathematics Volume 9, Issue 2, 4337–4375. most common combinations that usually appear based on fuzzy DEMATEL or VIKOR when the objective is to generate a comparative ranking based on customer sentiment. In addition, SA is being used with predictive algorithms, which from the available data try to predict future aspects such as demand or the image of a company from the sentiment of online reviews [13,14,86,98,100], and it is about achieving the greatest concreteness of information both in temporal and organizational terms. Hence, there is considerable interest in obtaining real-time data [15,81] and in its classification in different topics combining it with other techniques framed within natural language processing such as topics analysis with the LDA model (Latent Dirichlet Allocation) or directly performing aspect-based sentiment analysis (ABSA) [67,95,98]. 6. Conclusions The digitalization process is nowadays essential for organizations to maintain a sustainable competitive position in a constantly and rapidly changing market and, therefore, SA solutions are postulated as valuable tools that provide information in a fast and accessible way to improve the company and adopt customer-centric approaches. There has been a growing interest in recent years, popularizing this methodology as an alternative to traditional surveys to be less expensive and faster, or even immediate in real time. In the academic field, the research is focused on Asian countries such as China, India, and Pakistan and can be applied to any company that has textual data online, often from social networks or online review platforms. Articles have been found in different business sectors, especially in the hotel and catering sector. To date, this tool has been widely used for marketing and operations, but there are also more business areas where it can be applied. SA is usually combined with other methods to create models, such as the multicriteria decision making method or predictive algorithms. SA still has many limitations: obviously, it does not have the same capacity as a person to ascertain the sentiment expressed in a text, but it is clear that artificial intelligence is here to stay and will continue to evolve and improve its accuracy. Therefore, it seems essential to incorporate it into organizations, not to replace human capital but to facilitate and streamline the workflows of people when they have to make decisions and not fall into obsolescence in a highly competitive environment. Among these limitations of SA in its application to decision-making are several aspects. In addition to the limitations related to the method itself that were discussed in Section 2.1 “Sentiment analysis definition”, such as the focus on the English language, the difficulty in detecting irony and sarcasm, and the difficulty to discriminate between opposing sentiments toward different attributes in the same sentence [30], there are also limitations in its application to decision-making, mainly due to the constant activity and immediate change in the business world, which increasingly requires a faster analysis for organizations that even demand to do it in real time, and also in the format of the source, since much of the information is in videos and audio files, not just text as most analyses currently focus on. Another limitation is the complexity involved in customizing SA models for each organization or sector considering the jargon and the specific context they use, which will give greater precision to the analysis than using a generically trained model or a common lexicon. And, finally, rather oriented to the predisposition of organizations for the implementation of SA, it is observed that it is mainly used for marketing studies, forgetting other areas of the company that are susceptible to application. Use of AI tools declaration The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article. 4369 AIMS Mathematics Volume 9, Issue 2, 4337–4375. Conflict of interest The authors declare there is no conflict of interest. References 1. S. Kameswaran, V. S. F. Enigo, ScranViz: A tool for analytics and visualisation of unstructured data, Int. J. Bus. Intell. Data Min., 17 (2020), 179–192. https://doi.org/10.1504/IJBIDM.2020.108769 2. Y. Zhao, L. 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