Derbentsev, VasylДербенцев, Василь ДжоржовичДербенцев, Василий ДжорджевичBezkorovainyi, VitaliiБезкоровайний, Віталій СергійовичБезкоровайный, Виталий СергеевичMatviichuk, AndriiМатвійчук, Андрій ВікторовичМатвийчук, Андрей ВикторовичPomazun, OksanaПомазун, Оксана МиколаївнаПомазун, Оксана НиколаевнаHrabariev, AndriiГрабарєв, Андрій ВолодимировичГрабарев, Андрей ВладимировичHostryk, Oleksii2023-12-152023-12-152022-11Sentiment Analysis of Electronic Social Media Based on Deep Learning [Electronic resource] / Vasily D. Derbentsev, Vitalii S. Bezkorovainyi, Andriy V. Matviychuk [et al.] // Monitoring, Modeling Management of Emergent Economy : proceedings of 10th international conference on monitoring, modeling management of emergent economy (M3E2 2022). – Electronic text data. – 2022. – P. 163–175. – Title from screen.978-989-758-640-82975-9234https://ir.kneu.edu.ua/handle/2010/41646This paper describes Deep Learning approach of sentiment analyses which is an active research subject in the domain of Natural Language Processing. For this purpose we have developed three models based on Deep Neural Networks (DNNs): Convolutional Neural Network (CNN), and two models that combine convolutional and recurrent layers based on Long-Short-Term Memory (LSTM), such as CNN-LSTM and Bi-Directional LSTM-CNN (BiLSTM-CNN). As vector representations of words were used GloVe and Word2vec word embeddings. To evaluate the performance of the models, were used IMDb Movie Reviews and Twitter Sentiment 140 datasets, and as a baseline classifier was used Logistic Regression. The best result for IMDb dataset was obtained using CNN model (accuracy 90.1%), and for Sentiment 140 the model based on BiLSTM-CNN showed the highest accuracy (82.1%) correspondinly. The accuracy of the proposed models is a quite acceptable for practical use and comparable to state of the art models.enSentiment AnalysisSocial MediaDeep LearningConvolutional Neural NetworksLong Short-Term MemoryWord EmbeddingsArticle10.5220/0011932300003432