Symmetry/Asymmetry in Machine Learning and Data Science: Methods and Applications in Text Mining
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 29
Special Issue Editors
Interests: data mining; NLP; machine learning; opinion mining
Special Issue Information
Dear Colleagues,
Symmetry and asymmetry are foundational principles in understanding and modeling the structure and behavior of complex systems. In machine learning and data science—particularly in the domain of text mining—these concepts are increasingly being recognized for their profound implications in representation learning, pattern recognition, and model robustness.
Symmetrical structures underpin much of modern machine learning, from balanced neural architectures and data augmentation strategies to metric learning and clustering. Conversely, asymmetry often reflects the real-world heterogeneity found in linguistic data, hierarchical relationships, and task-specific imbalances. For example, text data commonly exhibit syntactic symmetry (e.g., parallel structures) and semantic asymmetry (e.g., cause–effect pairs), both of which challenge traditional modeling paradigms.
This Special Issue aims to explore the theoretical foundations, algorithmic innovations, and practical implications of symmetry and asymmetry in machine learning and data science, with a focus on text mining. We invite high-quality contributions that examine these dimensions across all stages of the text mining pipeline—from preprocessing and feature engineering to model design and evaluation. Topics of interest include, but are not limited to, the following:
- Symmetry-aware algorithms for text classification/clustering.
- Handling asymmetric data distributions (e.g., class imbalance, biased corpora).
- Graph-based text mining with symmetric/asymmetric node relationships.
- Ethical AI: mitigating asymmetry in fairness, transparency, and bias detection.
- Transformers and attention mechanisms balancing symmetry in contextual embeddings.
We encourage interdisciplinary contributions that leverage mathematical, statistical, or computational frameworks to address symmetry/asymmetry in textual data. Submissions may highlight theoretical insights, novel architectures, or real-world applications (e.g., healthcare, social media, legal text analysis).
We look forward to receiving your contributions.
Dr. Behnam Malmir
Dr. Bahrini Aram
Guest Editors
Manuscript Submission Information
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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. Symmetry 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 2400 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
- symmetry/asymmetry in NLP
- text classification and clustering
- imbalanced text data
- semantic relationships
- bias detection and fairness
- social media mining
- low-resource language processing
- dimensionality reduction
- bibliometric analysis
- web content mining
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