A comparative study of deep learning models for sentiment analysis of social media texts

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dc.contributor.authorDerbentsev, Vasyl
dc.contributor.authorДербенцев, Василь Джоржович
dc.contributor.authorБезкоровайний, Віталій Сергійович
dc.contributor.authorБезкоровайный, Виталий Сергеевич
dc.contributor.authorMatviichuk, Andrii
dc.contributor.authorМатвійчук, Андрій Вікторович
dc.contributor.authorМатвийчук, Андрей Викторович
dc.contributor.authorPomazun, Oksana
dc.contributor.authorПомазун, Оксана Миколаївна
dc.contributor.authorПомазун, Оксана Николаевна
dc.contributor.authorHrabariev, Andrii
dc.contributor.authorГрабарєв, Андрій Володимирович
dc.contributor.authorГрабарев, Андрей Владимирович
dc.contributor.authorHostryk, Oleksii
dc.date.accessioned2023-12-15T08:44:42Z
dc.date.available2023-12-15T08:44:42Z
dc.date.issued2022-11
dc.description.abstractSentiment analysis is a challenging task in natural language processing, especially for social media texts, which are often informal, short, and noisy. In this paper, we present a comparative study of deep learning models for sentiment analysis of social media texts. We develop three models based on deep neural networks (DNNs): a convolutional neural network (CNN), a CNN with long short-term memory (LSTM) layers (CNN-LSTM), and a bidirectional LSTM with CNN layers (BiLSTM-CNN). We use GloVe and Word2vec word embeddings as vector representations of words. We evaluate the performance of the models on two datasets: IMDb Movie Reviews and Twitter Sentiment 140. We also compare the results with a logistic regression classifier as a baseline. The experimental results show that the CNN model achieves the best accuracy of 90.1% on the IMDb dataset, while the BiLSTM-CNN model achieves the best accuracy of 82.1% on the Sentiment 140 dataset. The proposed models are comparable to state-of-the-art models and suitable for practical use in sentiment analysis of social media texts.
dc.identifier.citationA comparative study of deep learning models for sentiment analysis of social media texts [Electronic resource] / Vasily D. Derbentsev, Vitalii S. Bezkorovainyi, Andriy V. Matviychuk [et al.] // Monitoring, Modeling & Management of Emergent Economy (M3E2–MLPEED 2022) : proceedings of the selected and revised papers of 10th international conference, November 17–18, 2022, Virtual, Online (postponed due to Russian invasion of Ukraine) / [ed.: H. B. Danylchuk, S. O. Semerikov]. – Electronic text data. – Kryvyi Rih, 2022. – Vol. 3465 : Machine Learning for Prediction of Emergent Economy Dynamics. – P. 168–188. – Title from screen.
dc.identifier.issn1613-0073
dc.identifier.urihttps://ir.kneu.edu.ua/handle/2010/41647
dc.language.isoen
dc.publisherCEUR Workshop Proceedings
dc.subjectsentiment analysis
dc.subjectsocial media
dc.subjectdeep learning
dc.subjectconvolutional neural networks
dc.subjectlong short-term memory
dc.subjectword embeddings
dc.titleA comparative study of deep learning models for sentiment analysis of social media texts
dc.typeArticle
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