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Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed.

The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted.

It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums.

To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

The purpose of this paper is to review and compare selected software for data mining, text mining (TM), and web mining that are not available as free open‐source software.

Selected softwares are compared with their common and unique features. The software for data mining are SAS^® Enterprise Miner™, Megaputer PolyAnalyst^® 5.0, NeuralWare Predict^®, and BioDiscovery GeneSight^®. The software for TM are CompareSuite, SAS^® Text Miner, TextAnalyst, VisualText, Megaputer PolyAnalyst^® 5.0, and WordStat. The software for web mining are Megaputer PolyAnalyst^®, SPSS Clementine^®, ClickTracks, and QL2.

This paper discusses and compares the existing features, characteristics, and algorithms of selected software for data mining, TM, and web mining, respectively. These softwares are also applied to available data sets.

The limitations are the inclusion of selected software and datasets rather than considering the entire realm of these. This review could be used as a framework for comparing other data, text, and web mining software.

This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs.

Each of the software selected for this research has its own unique characteristics, properties, and algorithms. No other paper compares these selected softwares both visually and descriptively for all the three types of data, text, and web mining.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

In the architecture, engineering and construction (AEC) industry, technology developers have difficulties in fully understanding user needs due to the high domain knowledge threshold and the lack of effective and efficient methods to minimise information asymmetry between technology developers and AEC users. The paper aims to discuss this issue.

A synthetic approach combining domain knowledge and text mining techniques is proposed to help capture user needs, which is demonstrated using building information modelling (BIM) apps as a case. The synthetic approach includes the: collection and cleansing of BIM apps’ attribute data and users’ comments; incorporation of domain knowledge into the collected comments; performance of a sentiment analysis to distinguish positive and negative comments; exploration of the relationships between user sentiments and BIM apps’ attributes to unveil user preferences; and establishment of a topic model to identify problems frequently raised by users.

The results show that those BIM app categories with high user interest but low sentiments or supplies, such as “reality capture”, “interoperability” and “structural simulation and analysis”, should deserve greater efforts and attention from developers. BIM apps with continual updates and of small size are more preferred by users. Problems related to the “support for new Revit”, “import & export” and “external linkage” are most frequently complained by users.

The main contributions of this work include: the innovative application of text mining techniques to identify user needs to drive BIM apps development; and the development of a synthetic approach to orchestrating domain knowledge, text mining techniques (i.e. sentiment analysis and topic modelling) and statistical methods in order to help extract user needs for promoting the success of emerging technologies in the AEC industry.

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Flood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.

This investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.

Topic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.

This study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.