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Computation
Special Issues
Sentiment-Driven Modelling in Business, Economics, and Social Sciences
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Sentiment-Driven Modelling in Business, Economics, and Social Sciences

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4593

Special Issue Editor

Prof. Dr. Roman Makarov

E-Mail Website
Guest Editor
Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
Interests: mathematical and computational finance; data-driven modelling; predictive analytics; Monte Carlo methods; statistical modelling; stochastic analysis

Special Issue Information

Dear Colleagues,

The rapid increase in digital information has brought sentiment analysis to the forefront of advanced modelling and computation in the social sciences. News articles, corporate disclosures, policy reports, online forums, and social media platforms now provide rich sources of unstructured data that reflect collective perceptions, emotions, and expectations. Sentiment, unlike traditional quantitative indicators, captures the qualitative aspect of human behaviour that often influences decision-making in markets, organizations, and societies. Integrating such measures into statistical and computational frameworks could potentially significantly enhance predictive accuracy, risk assessment, and our understanding of complex economic and social phenomena.

This Special Issue will bring together contributions that advance the theory, methodology, and applications of sentiment-driven modelling and computation. It particularly encourages research that combines sentiment extraction with statistical methods, econometric analysis, and modern machine learning techniques to generate new insights into business, finance, economics, and other fields. It will also showcase both methodological innovation and practical relevance, providing a platform for interdisciplinary dialogue at the intersection of data science and the social sciences.

Potential topics of interest include, but are not limited to, the following:

  • Statistical and econometric models integrating sentiment measures;
  • Machine learning and natural language processing techniques for sentiment quantification;
  • Forecasting of financial markets, macroeconomic variables, and volatility using sentiment indicators;
  • Behavioural models of consumer, investor, or voter sentiment;
  • Sentiment analysis applied to marketing, supply chain management, and organizational performance;
  • Applications in political economy, policy evaluation, and social behaviour;
  • Advances in measuring uncertainty, interpretability, and robustness of sentiment-driven models;
  • Interdisciplinary approaches connecting linguistics, computational methods, and applied economics.

Prof. Dr. Roman Makarov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sentiment analysis
  • natural language processing (NLP)
  • machine learning
  • statistical modelling
  • behavioural economics
  • business and financial analytics
  • computational social science

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Published Papers (4 papers)

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Research

28 pages, 3621 KB  
Article
Evaluating Pre-Trained Transformer-Based Models for Political Sentiment Analysis on Social Media
by María Patricia Tzili Cruz, Salvador Contreras Hernández, José Martín Espínola Sánchez, Raúl Hernández Medina, Alma Alejandra Luna Gómez and Adriana Marlene Pacheco Orozco
Computation 2026, 14(6), 127; https://doi.org/10.3390/computation14060127 - 31 May 2026
Viewed by 238
Abstract
Sentiment analysis has broad applications in social media networks due to the high volume of user activity on diverse topics such as political debates. Transformer-based neural networks are among the technologies that achieve significant results in text classification. This study evaluates twelve pre-trained [...] Read more.
Sentiment analysis has broad applications in social media networks due to the high volume of user activity on diverse topics such as political debates. Transformer-based neural networks are among the technologies that achieve significant results in text classification. This study evaluates twelve pre-trained transformer-based models through fine-tuning for sentiment classification of Spanish-language political texts from the social media network X. Some of these models were originally created in Spanish, while others are multilingual models that include Spanish. The twelve models were trained to specialize in sentiment classification on political topics, using the same training and testing parameters, in order to compare them under equal conditions during fine-tuning. Good results were obtained with the precision, recall, and F1-score metrics mainly in multilingual models but also in some models originally created in Spanish. The study includes the detailed results of the evaluation in training and testing for the three metrics employed. Full article
(This article belongs to the Special Issue Sentiment-Driven Modelling in Business, Economics, and Social Sciences)
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30 pages, 9123 KB  
Article
Digital Attention as a Market Salience Indicator: Predicting Fintech Market Performance with Computational Models
by Vasilina K. Tsimpouka, Nikolaos T. Giannakopoulos and Damianos P. Sakas
Computation 2026, 14(5), 114; https://doi.org/10.3390/computation14050114 - 18 May 2026
Viewed by 317
Abstract
This study examines whether digital attention can serve as an engagement-based digital attention signal for fintech market performance. Using a revised panel of 70 firm-year observations from seven publicly verifiable fintech and payments firms over 2016–2025, the analysis combines financial outcomes, sector investment [...] Read more.
This study examines whether digital attention can serve as an engagement-based digital attention signal for fintech market performance. Using a revised panel of 70 firm-year observations from seven publicly verifiable fintech and payments firms over 2016–2025, the analysis combines financial outcomes, sector investment indicators, and digital variables related to web traffic, SEO visibility, social media presence, and app popularity. A Digital Attention Index (DAI) was constructed through arithmetic averaging and principal component analysis, with the first component explaining 82.39% of the digital-indicator variance. Fixed Effects models show that the DAI is positively and significantly associated with revenue, market capitalization, and net income, while sector investment is generally weak or insignificant. Out-of-sample validation confirms that panel Fixed Effects specifications outperform pooled OLS, Ridge, and Random Forest models. App popularity is the strongest standalone predictor for revenue and net income, while social media performs best for market capitalization. However, first-difference models weaken most relationships, and Granger tests indicate bidirectional temporal ordering, with financial performance often preceding digital attention. Overall, the findings support the DAI as a useful computational signal of fintech performance, while emphasizing that predictive and causal claims require cautious interpretation. Full article
(This article belongs to the Special Issue Sentiment-Driven Modelling in Business, Economics, and Social Sciences)
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31 pages, 652 KB  
Article
AI-Enabled Governance: Board Gender Diversity and Corporate Tax Avoidance
by Marwan Mansour, Mo’taz Al Zobi, Ahmad Marei, Luay Daoud and Nour Ibrahim Kurdi
Computation 2026, 14(5), 97; https://doi.org/10.3390/computation14050097 - 23 Apr 2026
Viewed by 614
Abstract
Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness [...] Read more.
Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness in constraining aggressive tax planning may depend on firms’ informational and technological environments. This study examines whether artificial intelligence (AI) capability strengthens the governance role of BGD in reducing corporate tax avoidance. Using a balanced panel of 1586 non-financial firms from developing economies over the period 2009–2023, the analysis employs firm FE models and dynamic two-step System GMM estimations to address unobserved heterogeneity, endogeneity, and the persistence of corporate tax behavior. The results indicate that BGD is positively associated with effective tax rates, implying lower levels of corporate tax avoidance. Furthermore, AI capability—measured using a lagged specification—significantly strengthens this relationship, suggesting that firms with higher AI adoption exhibit a stronger governance effect of gender-diverse boards on tax compliance. Additional robustness tests—including alternative tax avoidance measures, alternative BGD specifications, heterogeneity analysis, and selection-bias corrections using Heckman, propensity score matching (PSM), and instrumental variable (2SLS) approaches—confirm the stability of the findings. Overall, the results highlight the complementary role of technological capability and board diversity in strengthening corporate governance (CG) and fiscal discipline in developing economies. Full article
(This article belongs to the Special Issue Sentiment-Driven Modelling in Business, Economics, and Social Sciences)
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13 pages, 1009 KB  
Article
Phishing Email Detection Using BERT and RoBERTa
by Mariam Ibrahim and Ruba Elhafiz
Computation 2026, 14(2), 46; https://doi.org/10.3390/computation14020046 - 7 Feb 2026
Viewed by 2741
Abstract
One of the most harmful and deceptive forms of cybercrime is phishing, which targets users with malicious emails and websites. In this paper, we focus on the use of natural language processing (NLP) techniques and transformer models for phishing email detection. The Nazario [...] Read more.
One of the most harmful and deceptive forms of cybercrime is phishing, which targets users with malicious emails and websites. In this paper, we focus on the use of natural language processing (NLP) techniques and transformer models for phishing email detection. The Nazario Phishing Corpus is preprocessed and blended with real emails from the Enron dataset to create a robustly balanced dataset. Urgency, deceptive phrasing, and structural anomalies were some of the neglected features and sociolinguistic traits of the text, which underwent tokenization, lemmatization, and noise filtration. We fine-tuned two transformer models, Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa), for binary classification. The models were evaluated on the standard metrics of accuracy, precision, recall, and F1-score. Given the context of phishing, emphasis was placed on recall to reduce the number of phishing attacks that went unnoticed. The results show that RoBERTa has more general performance and fewer false negatives than BERT and is therefore a better candidate for deployment on security-critical tasks. Full article
(This article belongs to the Special Issue Sentiment-Driven Modelling in Business, Economics, and Social Sciences)
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