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Analytical review of methods for identifying emotions in text data
CEUR Workshop Proceedings. 2020. Vol. 2552. P. 8–21.
Dvoynikova A., Verkholyak O., Karpov A.
The sentiment analysis of text is one of the important tasks in the field of natural language processing. It is used in different areas. Despite the variety of existing methods, the systems of sentiment analysis of Russian-language texts give low accuracy compared to English-language ones. The article discusses basic methods for identifying emotions in text data and methods of text vectorization. The existing achievements in the field of computer sentiment analysis are analyzed. At the moment, there are many unsolved problems in the field of automatic sentiment analysis.
Konstantinov A., Elizarova Anastasiya P., Utkin L., Computing, Telecommunications and Control 2026 Vol. 19 No. 1 P. 16–25
Decision trees and their ensemble extensions, such as random forests, are widely used as classification models due to their simplicity and interpretability. However, in many real-world tasks where class labels overlap in the feature space, standard decision trees rely on hard splits that create fragile decision boundaries. In these regions, small perturbations in the input ...
Added: October 2, 2026
International Joint Conferences on Artificial Intelligence, 2026.
Added: October 1, 2026
I. D. Lazarev, Narozniak M., Byrnes T. et al., Physical Review A: Atomic, Molecular, and Optical physics 2025 Vol. 111 No. 012416 Article 012416
Unsupervised machine learning is one of the main techniques employed in artificial intelligence. We introduce an algorithm for quantum-assisted unsupervised data clustering using the self-organizing feature map, a type of artificial neural network. The complexity of our algorithm scales as 𝑂(𝐿𝑁), in comparison to the classical case which scales as 𝑂(𝐿𝑀𝑁), where 𝑁 is the ...
Added: September 14, 2026
Trubochkina N. K., М.: Юрайт, 2026.
This textbook is designed to develop students' holistic understanding of modern production processes and methods for their analysis and management using machine learning technologies. In the context of the fourth industrial revolution, where traditional engineering disciplines are inextricably intertwined with intelligent data processing methods, there is a growing need for specialists capable of integrating knowledge ...
Added: August 8, 2026
Ekaterina S. Podolskaia, Sinitsina A., European Journal of Forest Engineering 2026 Vol. 12 No. 1 P. 7–22
Machine learning in transport modeling has become a trend in science and industry. In this paper, we observe its main directions and focus on a dataset of seasonal road creation. Seasonality as a parameter in transport modeling has a significant impact on transport scenarios but is underestimated worldwide and in Russia, despite modern data challenges. ...
Added: June 24, 2026
Solovyev Roman A., Telpukhov Dmitry, Shafeev I. et al., Technologies 2026 Vol. 14 No. 3 Article 169
With the continuous scaling of semiconductor design technologies, evaluating static IR drop has become a critical bottleneck in the physical synthesis flow. This paper presents a machine learning-based framework that transforms the power delivery network (PDN) analysis problem into an image-to-image translation task using a U-Net architecture with MaxViT and EfficientNet encoders. By implementing a ...
Added: May 3, 2026
Lazarev M., Derkach D., Ratnikov F. et al., Scientific data 2026 Vol. 13 No. 1 Article 1003
In this study, we present a comprehensive set of experimental data aimed at uncovering the mechanisms and regularities governing the deformation behavior of composites reinforced with continuous carbon fibers (CF) based on thermoplastic polymers. This work describes data extraction techniques that can later be used to optimize the mechanical properties of such structures using neural ...
Added: April 23, 2026
Cham: Springer, 2026.
This book delivers actionable insights through 21 peer-reviewed chapters featuring new methods, models, and applications based on computational intelligence. Discover cutting-edge tools to support smart, efficient decision-making in complex, real-world scenarios. Organized into three parts—prescriptive analytics, soft computing models, and practical case studies—it spans domains such as healthcare, energy, mobility, finance, and public services. Readers ...
Added: March 17, 2026
Ilin E., Frolov N., Seferyan M. et al., Bioorganic Chemistry 2025 Vol. 167 Article 109175
The ongoing rise of resistant bacterial pathogens poses a significant threat to current antibacterials' effectiveness putting millions of people's lives at risk. However, modern machine learning (ML) tools promise to tip the scales in the never-ending development of antimicrobial agents' pipelines. Herein we present a novel approach for quaternary ammonium compounds (QACs) antibacterial activity prediction ...
Added: March 16, 2026
Kiselev G., Prokhorov A., Journal of Mathematical Sciences. Vol. 295, No. 2, December, 2025. Mathematical Modeling and AI for Traffic Flows on Networks and Related Topics 2025 No. 295 P. 185–196
We study the problem of estimating the population and workplaces in a given area using open data sources and machine learning algorithms for automation and improvement of quality and accuracy of the transport demand calculation in transport modeling.
Bibliography: 6 titles. Illustrations: 7 figures. ...
Added: March 12, 2026
Mikhail R. Samatov, Liu D., Emir S. Amirov et al., The Journal of Physical Chemistry Letters 2025 Vol. 16 No. 51 P. 13068–13074
Ion migration at grain boundaries (GBs) is a key issue leading to the performance degradation of metal halide perovskites (MHPs). Given the weak lattice interactions, the properties of MHPs are highly sensitive to external strain, which is inevitable in practical applications. Nevertheless, a fundamental understanding of the GB behavior under strain is still lacking. Using ...
Added: December 20, 2025
Springer, 2025.
This volume gathers the peer-reviewed proceedings of the Fifth France's International Conference on Complex Systems (FRCCS 2025), held in Bordeaux, France. FRCCS has become a key interdisciplinary venue for researchers and practitioners exploring the theory, modeling, and applications of complex systems.
The book covers a broad range of topics, including network science, dynamical systems, data mining, ...
Added: December 8, 2025
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2025. P. 987–991.
Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and ...
Added: November 4, 2025
Dalian: IEEE, 2025.
The increasing complexity of modern software development necessitates intelligent, automated security analysis frameworks that can effectively pay attention of human on high-risk software releases. This paper introduces a Multi Agent System (MAS) framework designed to enhance the security assessment process by leveraging artificial intelligence (AI) and intelligent computing for real-time release analysis. The proposed system ...
Added: November 3, 2025
Morozov N., Maximov I., Tiapkin D. et al., , in: Volume 267: International Conference on Machine Learning, 13-19 July 2025, Vancouver Convention Center, Vancouver, CanadaVol. 267.: [б.и.], 2025. P. 44887–44910.
Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects from a given probability distribution, potentially known up to a normalizing constant. Instead of working in the object space, GFlowNets proceed by sampling trajectories in an appropriately constructed directed acyclic graph environment, greatly relying on the acyclicity of the graph. ...
Added: October 15, 2025
[б.и.], 2025.
Added: October 15, 2025
Chalykh O., Korogod D., Ivan S. Novikov et al., Journal of Chemical Physics 2025 Vol. 163 No. 13 Article 134112
In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential, for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We demonstrate that the explicit incorporation of dispersion interactions via D2 and ...
Added: October 12, 2025
Sreejith S., Pruzhinskaya M., Volnova A. et al., New Astronomy 2026 Vol. 122 Article 102466
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and
degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine
learning algorithms, which often require a labelled training set to learn data patterns. We present an expertlabelled dataset of 1127 artefacts with 1213 labels from ...
Added: October 2, 2025
Voskoboynikov A., Magomed Aliverdiev, Yulia Nekrasova et al., Journal of Neural Engineering 2025 Vol. 22 No. 5 Article 056002
Objective. The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like electrocortical stimulation mapping (ESM) are effective but carry a significant risk of inducing seizures. Methods. To address this, we have prepared a comprehensive ESM + electrocorticographic mapping (ECM) dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then ...
Added: September 2, 2025
Derkacheva A., Frost G., Epstein H. et al., Journal of Ecology 2025 Vol. 113 No. 10 P. 2813–2831
Tundra shrub expansion is a central form of change in warming Arctic ecosystems, but the pace of shrubification varies across spatial scales, complicating efforts to understand its drivers and consequences. Here, we apply convolutional neural networks (CNNs) to very-high resolution satellite image pairs acquired 10–15 years apart (circa 2005–2019) to identify spatio-temporal patterns of tall shrub ...
Added: August 4, 2025
Matkin N., Smirnov A., Usanin M. et al., , in: 12th International Conference, AIST 2024, Bishkek, Kyrgyzstan, October 17–19, 2024, Revised Selected Papers.: Cham: Springer, 2025.
The labor market is undergoing rapid changes, with increasing demands on job seekers and a surge in job openings. Identifying essential skills and competencies from job descriptions is challenging due to varying employer requirements and the omission of key skills. This study addresses these challenges by comparing traditional Named Entity Recognition (NER) methods based on ...
Added: July 26, 2025
Sotskiy Arseniy, Tsyrulnikov M., Spatial Statistics 2024 Vol. 64
The sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this condition is never met. As a result, in high dimensions the Ensemble Kalman Filter’s (EnKF) ensemble does not contain enough information ...
Added: June 20, 2025