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This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

This study aims to deepen the understanding of consumer engagement and satisfaction within the health and wellness tourism sector, a rapidly growing niche in the global tourism industry. It focuses on identifying key elements that influence consumer perceptions and experiences in this domain.

Employing a quantitative approach, this research utilizes Dynamic Correlated Topic Models (DCTM) and sentiment analysis techniques to analyze user-generated content from TripAdvisor. The methodology involves parsing through extensive online reviews to extract thematic patterns and emotional sentiments related to various wellness tourism experiences.

The findings reveal that wellness and relaxation, spa and therapy services, and cultural immersion are significant factors influencing consumer satisfaction in health and wellness tourism. These elements contribute to a more profound and emotionally satisfying tourist experience, highlighting the shift from traditional tourism to more holistic, wellness-focused travel.

The study is limited by its focus on user-generated content from a single platform, which may not fully represent the diverse range of consumer experiences in health and wellness tourism. Future research could expand to include other platforms and cross-reference with qualitative data.

The study offers valuable implications for destination managers and marketers in the health and wellness tourism industry, suggesting that enhancing and promoting wellness-centric experiences can significantly improve consumer satisfaction and engagement.

The research underscores the growing importance of health and wellness in societal values, reflecting a shift in consumer preferences towards travel experiences that offer mental, physical, and spiritual benefits. This has broader implications for how destinations can cater to the evolving demands of socially conscious travelers.

This research contributes original insights into the evolving field of health and wellness tourism by integrating advanced text mining techniques to analyze consumer feedback, offering a novel perspective on what drives engagement and satisfaction in this sector.

This study aims to examine values derived from apps and their relationship with continual intention using reviews from the Google Play Store.

This paper delves deep into the determinants of mobile health apps’ (MHAs) value offering (functional, social, epistemic, conditional and hedonic value) using automatic content analysis and text mining of user reviews. This paper obtained data from a sample of 45,019 MHA users who have posted reviews on the Google Play Store. This paper analyzed the data using text mining, ACA and regression techniques.

The findings show that values moderate the relationship between review length and ratings. This paper found that the higher the length, the lower the ratings and vice versa. This paper also demonstrated that the novelty and perceived reliability of the app are the two most essential constructs that drive user ratings of MHAs.

This is one of the first studies, to the best of the authors’ knowledge, that derives values (functional, social, epistemic, conditional and hedonic value) using text mining and explores the relationship with user ratings.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.

This research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience.

Text mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint.

The study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations.

The dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement.

In e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development.

Drawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

This study investigates the influence of corporate culture on financial reporting transparency within Iranian firms.

Leveraging a dataset of 1,480 firm-year observations from the Tehran Stock Exchange spanning from 2013 to 2022, the study employs text mining to quantify linguistic features of corporate culture and transparency, specifically readability and tone, within annual financial statements and Management Discussion and Analysis (MD&A) reports.

Our results confirm a positive and significant relationship between corporate culture and financial reporting transparency. The distinct dimensions of corporate culture — Creativity, Competition, Control, and Collaboration — each uniquely enhance financial transparency. Robustness tests including firm fixed-effects, entropy balancing, Generalized Method of Moments (GMM), and Propensity Score Matching (PSM) validate the profound influence of corporate culture on transparency. Additionally, our analysis shows that corporate culture significantly affects the disclosure of business, operational, and financial risks, with varying impacts across risk categories. Cross-sectional analysis further reveals how the impact of corporate culture on transparency varies significantly across different industries and firm sizes.

The study’s scope, while focused on Iran, opens avenues for comparative research in different cultural and regulatory environments. Its reliance on text mining could be complemented by qualitative methods to capture more nuanced linguistic subtleties.

Findings underscore the strategic importance of cultivating a transparent corporate culture for enhancing financial reporting practices and stakeholder trust, particularly in emerging economies with similar dynamics to Iran.

This research is pioneering in its quantitative analysis of the textual features of corporate culture and its impact on transparency within Iranian corporate reports, integrating foundational theoretical perspectives with empirical evidence.