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Business and Government Applications of Text Mining & Natural Language Processing (NLP) for Societal Benefit

Business and Government Applications of Text Mining & Natural Language Processing (NLP) for Societal Benefit

Impact Score 5.12


Special Issue Information

Submission Deadline: 30-06-2021
Journal Impact Score: 5.12
Journal Name: Decision Support Systems
Publisher: Decision Support Systems

Special Issue Call for Papers

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that helps computers to understand, process, and analyze large amounts of natural human language data (Kang et al., 2020). The concepts of AI, machine learning (ML), and human-to-machine interactions have been prevalent for several decades. A growing number of businesses have been using advanced analytics and ML methods for resolving their business concerns, but NLP is emerging as one of the most popular and indispensable technologies for businesses today. NLP and text mining are generally used synchronously for achieving different goals. NLP has the ability to work with all natural human communication variables including text, audio, and video, whereas text mining deals with the analysis of textual datasets to discover novel and useful patterns and trends. The most common business application that concurrently uses NLP and text mining is social media monitoring, where businesses rely on these technologies to understand the sentiments (i.e. mood and the emotions) of the customers by analyzing the user generated data (Kang et al., 2020). Smart use of NLP empowers businesses to gain a competitive edge over their rivals in modern market spaces. According to a recent estimate, around 80% of business data is non-actionable due to being unstructured in nature (Bahja, 2020). As the unstructured data volume overloads several sectors in business and government (e.g. finance, e-commerce, healthcare, hospitality sectors in business, and business classification, request for proposals (RFP), trust and confidence, public comments in government areas), NLP and other AI technologies are becoming increasingly crucial for business growth and survival. Several emerging NLP business applications have the ability to revamp and revitalize struggling businesses, by demystifying obscure statistical business reports into precise actionable data, leading to streamlined action plans and higher revenues in the long run.

One of the biggest challenges faced by businesses is to understand human behaviors. NLP and text mining have enabled e-commerce platforms to extract attributes and hence improve product searches to target the right customers. Recently, Amazon claimed to generate 35% of its revenue by providing targeted product recommendations (Bahja, 2020). NLP recommendation algorithms have shifted from traditional keyword paradigms to taking customer’s internet search history, location, context, and personalized affiliations into consideration. These real time insights have aided retailers to personalize product recommendations to an individual customer, to understand how consumers are using their products, while simultaneously helping online buyers to see relevant products matching their requirements (Nguyen et al., 2020).

NLP has played a vital role in improving the performance of healthcare systems. ML and NLP are being integrated with traditional healthcare practices, like clinical diagnosis and proposed treatments, creating a digitalized healthcare that is highly intuitive and robust. NLP has proven to be extremely successful in improving the healthcare process by effectively interpreting clinical notes. Data is collected and interpreted from various diagnostic reports, symptoms, EHR, patient discharge summaries, lab reports and doctors’ prescriptions, and presented to medical consultants for a sound decision making (Mandelbaum et al., 2018; Ahsen, Ayvaci and Raghunathan, 2019; Bahja, 2020).

The impact of online reviews on businesses has grown significantly during the last few years in several sectors such as e-governance, e-commerce, and hospitality industry (Wu, Lou, et al., 2019; Kang et al., 2020; Lu et al., 2020; Wu et al., 2020). Governments across the world are using text mining to decrease the interaction gap between citizens and the government and improve government services. Governments also collect a huge amount of textual data in the form of applications for permits, website feedback, stakeholder interviews, and social media responses. NLP techniques could help governments better analyze feedback, increase regulatory compliance, and enhance policy analysis – all of which could benefit society (Cuffe et al., 2019, Jung and Suh, 2019; Kang et al., 2020).

Another application of NLP technology is to identify fake news propagators. In recent years, fake news created by manipulated images, text, audio, and videos has become a global phenomenon due to its explosive distribution, particularly on social media (Papanastasiou, 2020). Some people or groups often spread fake news through social media platforms to influence elections, initiate propaganda, spread violence, incite riots, or to humiliate others. Several governments and businesses are using NLP, text mining, machine learning, and deep learning techniques to fight the fake news menace and to identify the fake news propagators (Papanastasiou, 2020).

The aim of this special issue is to highlight novel and high-quality research in data science and business analytics, and to examine the current and future impact of NLP, text mining, big data analytics, and related technologies including machine learning and deep learning in businesses, government and society. We wish to bridge the gap between managerial and technical perspectives, and to publish articles that make a significant research contribution to NLP and text mining applications in business industries, government and society by taking a strategic point of view on AI. All managerial, technical and strategic perspectives and methods are welcome, including (but not to limited to) strategic, behavioral, statistical and economic analysis approaches. Methodologically, we embrace a variety of methods, including applied research, field experiments, quantitative research, and secondary data analysis.

"Please note that we are particularly interested in such papers that focus on the societal benefits of research conducted. All articles that simply focus on improving the accuracy of machine learning classifiers, without highlighting the benefit to the organization, are strictly not encouraged".

Dr. Ajay Kumar
Assistant Professor of Business Analytics & Information Systems
AIM Research Center on AI in Value Creation
EMLYON Business School, France
Email: [email protected]

​​Dr. Maryam Ghasemaghaei
Assistant Professor of Information Systems
DeGroote School of Business
McMaster University, Hamilton, ON, Canada
Email: [email protected]

Prof. Eric W. T. Ngai
Department of Management & Marketing
The Hong Kong Polytechnic University, Hong Kong
Email: [email protected]

​Prof. Sudip Bhattacharjee
Senior Research Fellow for Data Analytics, US Census Bureau
Professor, Operations and Information Management Department
School of Business, University of Connecticut, USA
Email: [email protected]

Prof. Dursun Delen
Patterson Foundation Chair in Business Analytics
Department of Management Science and Information Systems
Spears School of Business, Oklahoma State University, USA
Email: [email protected]

Submission Guidelines


Ahsen, M.E., Ayvaci, M.U.S., Raghunathan S. (2019). When algorithmic predictions use human-generated data: a bias-aware classification algorithm for breast cancer diagnosis, Information Systems Research, 30 (1), 97–116.

Bahja M. (May 11 2020). Natural Language Processing Applications in Business [Online First], IntechOpen, DOI: 10.5772/intechopen.92203. Available from: https://www.intechopen.com/online-first/natural-language-processing-applications-in-business

Cuffe, J., Bhattacharjee, S., Etudo, U., Smith, J.S., Basdeo, N., Burbank, N., Roberts, S.R., “Using Public Data to Generate Industrial Classification Codes”, NBER CRIW Conference (Conference on Research in Income and Wealth): Big Data for 21st Century Economic Statistics, Bethesda, MD, March 15-16, 2019.

Jung, Y. and Suh, Y. (2019). Mining the voice of employees: a text mining approach to identifying and analyzing job satisfaction factors from online employee reviews, Decision Support Systems, Vol. 123, p. 113074.

Kang, Y., Cai, Z., Tan, C.W., Huang, Q., and Liu H. (2020). Natural language processing (NLP) in management research: A literature review, Journal of Management Analytics, 7 (2) (2020), pp. 1-34.

Lu, X., He S., Lian S., Ba Sulin, and Wu J. (2020). Is user-generated content always helpful? The effects of online forum browsing on consumers' travel purchase decisions, Decision Support Systems, Vol. 137, p. 113368.

Mandelbaum A, Momcilovic P, Trichakis N. (2018). Data-driven appointment-scheduling under uncertainty: the case of an infusion unit in a cancer center. Management Science, 66(1), 243-270.

Nguyen, H., Calantone, R., and Krishnan, R. (2020). Influence of Social Media Emotional Word of Mouth on Institutional Investors’ Decisions and Firm Value, Management Science, 66 (2), pages 887-910.

Papanastasiou, Y. (2020). Fake news propagation and detection: A sequential model. Management Science, 66(5), 1826–1846.

Wu, L., Lou, B., & Hitt, L. (2019). Data analytics supports decentralized innovation. Management Science, 65(10), 4863–4877.

Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems, 132, 113280.

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