Пятикоп Е. Е. Comparison of the properties of learning semantic relationships between words of a natural language by models of the Word2Vec method in the problem of sentiment analysis.
Keywords:
sentiment analysis, mood determination, social media post, vector representation, machine learning, skip-gram, CBOW.
Abstract
This article is devoted to the research of the effective sentiment analysis method of English-language posts from social networks, based on the conversion of words into vector representations using the Word2Vec method. The paper describes and analyzes the existing methods of sentiment analysis, analyzes the Continuous Bag of Words (CBOW) and Skip-gram models as part of the Word2Vec method, provides a comparison of their properties when establishing semantic relationships between words in a natural language. An experimental study of the use of these models for various training functions is described.
References
Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012;
Walaa Medhat, Ahmed Hassan, Hoda Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal, Volume 5, Issue 4, 2014, Pages 1093-1113, ISSN 2090-4479;
Dmitry Shyngalov, Yelyzaveta Meleshko, Roman Mynaylenko, Vitaliy Reznichenko. Methods of automated sentiment analysis on social networks, Machinery in agricultural production, industry machine building, automation, 2017, Col.30, ISSN 2409-9392;
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. arXiv:1310.4546v1;
Muhamedyev, Ravil. (2015). Machine learning methods: An overview. CMNT. 19. 14-29;
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean. Logistic Regression Relating Patient Characteristics to Outcomes Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781v3;
Tim O’Keefe, Irena Koprinska. Feature Selection and Weighting in Sentiment Analysis. DOI:10.1.1.709.1463;
Oscar Blessed, Deho & Agangiba, A. & Aryeh, Felix & Ansah, Jeffery. (2018). Sentiment Analysis with Word Embedding. 1-4. 10.1109/ICASTECH.2018.8506717.
Sidorov G., Velasquez F., Stamatatos E., Gelbukh A., Chanona-Hernández L. (2013) Syntactic Dependency-Based N-grams as Classification Features. In: Batyrshin I., Mendoza M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science, vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_1
Mohammed, A.A., & Umaashankar, V. (2018). Effectiveness of Hierarchical Softmax in Large Scale Classification Tasks. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1090-1094.
Kaji, N., & Kobayashi, H. (2017). Incremental Skip-gram Model with Negative Sampling. EMNLP.
Walaa Medhat, Ahmed Hassan, Hoda Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal, Volume 5, Issue 4, 2014, Pages 1093-1113, ISSN 2090-4479;
Dmitry Shyngalov, Yelyzaveta Meleshko, Roman Mynaylenko, Vitaliy Reznichenko. Methods of automated sentiment analysis on social networks, Machinery in agricultural production, industry machine building, automation, 2017, Col.30, ISSN 2409-9392;
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. arXiv:1310.4546v1;
Muhamedyev, Ravil. (2015). Machine learning methods: An overview. CMNT. 19. 14-29;
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean. Logistic Regression Relating Patient Characteristics to Outcomes Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781v3;
Tim O’Keefe, Irena Koprinska. Feature Selection and Weighting in Sentiment Analysis. DOI:10.1.1.709.1463;
Oscar Blessed, Deho & Agangiba, A. & Aryeh, Felix & Ansah, Jeffery. (2018). Sentiment Analysis with Word Embedding. 1-4. 10.1109/ICASTECH.2018.8506717.
Sidorov G., Velasquez F., Stamatatos E., Gelbukh A., Chanona-Hernández L. (2013) Syntactic Dependency-Based N-grams as Classification Features. In: Batyrshin I., Mendoza M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science, vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_1
Mohammed, A.A., & Umaashankar, V. (2018). Effectiveness of Hierarchical Softmax in Large Scale Classification Tasks. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1090-1094.
Kaji, N., & Kobayashi, H. (2017). Incremental Skip-gram Model with Negative Sampling. EMNLP.
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Published
2021-12-23
How to Cite
Harbuzenko , O., & Piatykop , O. (2021). Пятикоп Е. Е. Comparison of the properties of learning semantic relationships between words of a natural language by models of the Word2Vec method in the problem of sentiment analysis. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (45), 59-65. https://doi.org/10.36910/6775-2524-0560-2021-45-09
Section
Computer science and computer engineering
