Standard

Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts. / Palchunov, Dmitry E.; Akhmedov, Ergash Yu.

Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1460-1465.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Palchunov, DE & Akhmedov, EY 2023, Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts. in Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), pp. 1460-1465, 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, Новосибирск, Russian Federation, 10.11.2023. https://doi.org/10.1109/apeie59731.2023.10347569

APA

Palchunov, D. E., & Akhmedov, E. Y. (2023). Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts. In Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023 (pp. 1460-1465). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/apeie59731.2023.10347569

Vancouver

Palchunov DE, Akhmedov EY. Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts. In Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 1460-1465 doi: 10.1109/apeie59731.2023.10347569

Author

Palchunov, Dmitry E. ; Akhmedov, Ergash Yu. / Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts. Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. pp. 1460-1465

BibTeX

@inproceedings{423803d717794576b83538a1f4ec11b4,
title = "Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts",
abstract = "The article is devoted to the problem of extracting emotional assessments from natural language texts. The article analyzes existing methods of sentiment analysis and explores modern methods of natural language processing to determine the sentiment of Uzbek texts. Existing approaches to the study and description of emotions are explored. Particular attention is paid to emotional assessments of situations presented in natural language texts. Logical-semantic methods and technologies based on deep machine learning methods are considered. Formalization of emotional assessments contained in natural language texts is carried out using the theory of partial models. The task of classification is to assign a class to objects based on their characteristics. In the case of the emotion model, the emotion texts are object-class pairs. Since the number of emotions is not binary, the classification is not binary. To solve the classification problem, a training data set consisting of labeled text with emotions is required.",
author = "Palchunov, {Dmitry E.} and Akhmedov, {Ergash Yu.}",
note = "The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no. FWNF-2022-0011).; 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023 ; Conference date: 10-11-2023 Through 12-11-2023",
year = "2023",
doi = "10.1109/apeie59731.2023.10347569",
language = "English",
isbn = "9798350330885",
pages = "1460--1465",
booktitle = "Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

RIS

TY - GEN

T1 - Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts

AU - Palchunov, Dmitry E.

AU - Akhmedov, Ergash Yu.

N1 - Conference code: 16

PY - 2023

Y1 - 2023

N2 - The article is devoted to the problem of extracting emotional assessments from natural language texts. The article analyzes existing methods of sentiment analysis and explores modern methods of natural language processing to determine the sentiment of Uzbek texts. Existing approaches to the study and description of emotions are explored. Particular attention is paid to emotional assessments of situations presented in natural language texts. Logical-semantic methods and technologies based on deep machine learning methods are considered. Formalization of emotional assessments contained in natural language texts is carried out using the theory of partial models. The task of classification is to assign a class to objects based on their characteristics. In the case of the emotion model, the emotion texts are object-class pairs. Since the number of emotions is not binary, the classification is not binary. To solve the classification problem, a training data set consisting of labeled text with emotions is required.

AB - The article is devoted to the problem of extracting emotional assessments from natural language texts. The article analyzes existing methods of sentiment analysis and explores modern methods of natural language processing to determine the sentiment of Uzbek texts. Existing approaches to the study and description of emotions are explored. Particular attention is paid to emotional assessments of situations presented in natural language texts. Logical-semantic methods and technologies based on deep machine learning methods are considered. Formalization of emotional assessments contained in natural language texts is carried out using the theory of partial models. The task of classification is to assign a class to objects based on their characteristics. In the case of the emotion model, the emotion texts are object-class pairs. Since the number of emotions is not binary, the classification is not binary. To solve the classification problem, a training data set consisting of labeled text with emotions is required.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182263957&origin=inward&txGid=a9055cef0bbc62e8289666f60dda9ecc

UR - https://www.mendeley.com/catalogue/9004bee1-f507-3bd6-b2da-5dc3965400c8/

U2 - 10.1109/apeie59731.2023.10347569

DO - 10.1109/apeie59731.2023.10347569

M3 - Conference contribution

SN - 9798350330885

SP - 1460

EP - 1465

BT - Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering

Y2 - 10 November 2023 through 12 November 2023

ER -

ID: 59614003