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Neuro-Fuzzy Modeling Techniques in Economics

Neuro-Fuzzy Modeling Techniques in Economics

ISSN 2415-3516
Дербенцев Василь  
Vasyl Derbentsev  
Безкоровайний Віталій  
Vitalii Bezkorovainyi  
Ахмедов Ренат  
Renat Akhmedov  

Підхід на основі машинного навчання до аналізу емоційної полярності електронних соціальних медіа

Machine learning approach of analysis of emotional polarity of electronic social media

DOI:

10.33111/nfmte.2020.095

Анотація: У статті пропонується новий підхід до оцінки емоційної полярності (або аналізу настроїв) електронних текстів у соціальних мережах. Для цього використовувалися як класичні методи машинного навчання (логістична регресія та метод опорних векторів), так і інструментарій глибоких нейронних мереж (повнозв’язні та згорткові нейромережі). Векторне представлення ґрунтувалось на частотних та попередньо навчених вкладеннях слів Word2vec і GloVe (з розмірами вкладення 100 і 300).
Для вибраного англомовного набору даних IMDb Movie Reviews точність класифікації за допомогою моделі логістичної регресії становила 87%, машини опорних векторів – 87,5%, повнозв’язної нейронної мережі – 88% і згорткової мережі – 90%. Точність запропонованих моделей є цілком прийнятною для практичних ситуацій і не поступається передовим рішенням у сфері обробки природньої мови за напрямом аналізу настроїв, що відкриває обнадійливі перспективи для подальших досліджень
Abstract: This paper proposes a new approach to evaluating the emotional polarity (or Sentiment Analysis) of electronic social media texts. For this purpose both conventional Machine Learning (Logistic Regression and Support Vector Machine), and Deep Neural Networks approaches (Fully Connected and Convolutional Neural Networks) were used. As vector representations of words, we used both the frequency-based and pretrained words embeddings Word2vec and GloVe (with embedding dimensions of size 100 and 300).
For the selected English-language IMDb Movie Reviews dataset the classification accuracy using the Logistic Regression model was 87%, the Support Vector Machine – 87.5%, the Fully Connected Neural Network – 88%, and the Convolutional Network – 90%. The accuracy of the proposed models is a quite acceptable for practical use-cases and is not inferior to cutting-edge Natural Language Processing solutions in the field of Sentiment Analysis, which opens up good prospects for further research.
Ключові слова: виявлення емоційної полярності, аналіз настроїв, електронні соціальні медіа, машинне навчання, глибинне навчання
Key words: emotional polarity detection, sentiment analysis, electronic social media, machine learning, deep learning
УДК: 519.767.6
UDC: 519.767.6

JEL: C45 C53 C89

To cite paper
In APA style
Derbentsev, V., Bezkorovainyi, V., & Akhmedov, R. (2020). Machine learning approach of analysis of emotional polarity of electronic social media. Neuro-Fuzzy Modeling Techniques in Economics, 9, 95-137. http://doi.org/10.33111/nfmte.2020.095
In MON style
Дербенцев В., Безкоровайний В., Ахмедов Р. Підхід на основі машинного навчання до аналізу емоційної полярності електронних соціальних медіа. Нейро-нечіткі технології моделювання в економіці. 2020. № 9. С. 95-137. http://doi.org/10.33111/nfmte.2020.095 (дата звернення: 09.07.2025).
With transliteration
Derbentsev, V., Bezkorovainyi, V., Akhmedov, R. (2020) Pidkhid na osnovi mashynnoho navchannia do analizu emotsiinoi poliarnosti elektronnykh sotsialnykh media [Machine learning approach of analysis of emotional polarity of electronic social media]. Neuro-Fuzzy Modeling Techniques in Economics, no. 9. pp. 95-137. http://doi.org/10.33111/nfmte.2020.095 (accessed 09 Jul 2025).
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