Text Mining and Big Data Analysis

Dear Colleagues,

With the growth of digital technology, vast amounts of text data—including social media posts, electronic health records, academic papers, and business documents—are generated daily. The technologies of “text mining,” which extract valuable insights from unstructured data, and “big data analytics,” which support it, play an indispensable role in decision-making in modern society. Additionally, modern document data is not merely a list of text. In a variety of documents, such as academic papers, technical reports, business materials, and web pages, figures, tables, and images are effectively arranged to complement the text, conveying sophisticated information. Consequently, topics such as analyzing the correlations between visual elements (e.g., figures, tables, and images) and text within documents, utilizing layout information, and conducting multimodal big data analysis that integrates these elements have become important areas of research, extending beyond the scope of traditional text mining.    

For this call for papers, we invite submissions of innovative methods and application examples to understand the overall structure of documents and extract advanced insights. We also broadly invite research on the theory and applications of text mining and big data analysis, ranging from the latest methods in natural language processing (NLP) and multimodal data analysis to applications of large language models (LLMs), efficient large-scale data processing infrastructures, and even privacy protection and ethical issues. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: 

1. Document Intelligence & Multimodal Analysis

• Table & Figure Understanding: Automatic recognition of table structures, linking captions to text in figures, and numerical extraction from graph data. 
• Layout-aware Analysis: Semantic analysis that takes visual layout (font, position, proximity) into account. 
• Cross-modal Search and Generation: Text summarization based on the content of tables and figures, or automatic search and recommendation of tables and figures that match the text. 
• VQA (Visual Question Answering) for Documents: Natural language question-answering technology regarding figures and tables within documents. 

2. Advanced Text and Data Mining

• Document understanding through the integration of large language models (LLMs) and visual language models (VLMs).
• Extraction of structured knowledge from unstructured data (images, PDFs) (knowledge graph construction).
• Detection of inconsistencies between text and figures/tables within documents, and reliability assessment.

3. Theories and Algorithms

• Advanced development and tuning techniques for Large Language Models (LLMs)
• Sentiment analysis, opinion extraction, and intent understanding
• Multilingual and cross-lingual text mining
• Structuring unstructured data and knowledge graph construction
• Anomaly detection and pattern recognition in text data

4. Big Data Infrastructure & Analytics

• Distributed and parallel computing algorithms for large-scale text processing
• Text analysis of real-time stream data
• Scalability and efficiency in big data analysis
• Graph mining and network analysis

5. Social Challenges & Ethics

• Privacy-Preserving Text Mining (Differential Privacy, Secure Computation, etc.)
• AI Transparency, Explainability (XAI), and Fairness
• Fake News Detection and Information Reliability Assessment

6. Applications

• Technical Documents: Automatic analysis of complex manuals, design drawings, and specifications. 
• Digital Archives and Historical Materials: Transcription and contextual analysis of historical documents, including illustrations. 
• Science Mining: Integrated analysis of experimental data (graphs and tables) and theories (text) from vast collections of academic papers. 
• IoT and Manufacturing: Anomaly diagnosis and maintenance support by integrating sensor logs with operational manuals (illustrations). 
• Healthcare and Well-being: Analysis of medical billing statements and electronic health records 
• Education Technology (EdTech): Learning support through analysis of learning logs and open-ended responses 
• Business and Finance: Market forecasting, analysis of customer reviews, and risk management 
• Public Policy: Visualization of public opinion and information extraction during disasters 

Dr. Tsunenori Mine
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-anonymized 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 and data mining
• big data analysis
• multi-modal analysis
• natural language processing
• machine learning
• deep learning
• large language model
• vision language model