Natural Language Processing and Large-Scale Semantic Data Mining for Business Performance and Strategic Decision-Making

Dear Colleagues,

The rapid expansion of digital information has generated unprecedented opportunities to understand how firms create, communicate and sustain competitive advantage through data-driven insights. Advances in Natural Language Processing (NLP), artificial intelligence (AI) and semantic data mining enable researchers and practitioners to extract meaningful patterns from unstructured data, including corporate disclosures, patents, news and social media. These approaches are reshaping how we evaluate determinants of organizational performance, innovation capacity and sustainability strategies. 

This Special Issue aims to showcase cutting-edge research in Big Data analytics, natural language processing (NLP), artificial intelligence (AI) and semantic network analysis, with a focus on leveraging large-scale text data to enhance firm performance, strategic decision-making and market intelligence. We welcome both theoretical and empirical contributions exploring how semantic and linguistic data analysis can provide new perspectives on:

• corporate strategy and performance evaluation;
• innovation and technological trajectories;
• ESG and sustainability reporting;
• financial and risk forecasting;
• organizational learning and knowledge management.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Advanced NLP and semantic mining techniques applied to business and management research;

• Large-scale text analytics for evaluating firm performance and competitive dynamics;
• Semantic network construction and network-based approaches to corporate data;
• Brand or reputation analysis through news, patents or social media;
• AI-based decision support systems using linguistic and semantic information;
• Integration of structured (e.g., accounting, market) and unstructured (e.g., textual) data;
• Explainable AI and interpretable NLP models for corporate applications;
• Applications of semantic similarity, topic modelling or large language models (LLMs) in business contexts.

We look forward to receiving your contributions. 

Dr. Ludovica Segneri
Dr. Andrea Fronzetti Colladon
Prof. Dr. Francesca Grippa
Dr. Aleksandra Przegalińska
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Big Data and Cognitive Computing 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.

• text mining
• natural language processing
• semantic network analysis
• artificial intelligence
• business performance
• data-driven business analytics