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Models that use rule-based and machine learning-based techniques have become popular. However, existing models have been under-performing in classifying irony, sarcasm, and subjectivity in the text. In this paper, we aim to deploy and evaluate the performances of the State-of-the-Art machine learning sentiment analysis techniques on a public IMDB dataset. The dataset includes many samples of irony and sarcasm. Long-short term memory (LSTM), bag of tricks (BoT), convolutional neural networks (CNN), and transformer-based models are developed and evaluated. In addition, we have examined the effect of hyper-parameters on the accuracy of the models.<\/jats:p>","DOI":"10.1007\/978-3-031-11432-8_3","type":"book-chapter","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T13:18:01Z","timestamp":1658927881000},"page":"34-42","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Sentiment Analysis Using State of the Art Machine Learning Techniques"],"prefix":"10.1007","author":[{"given":"Salih","family":"Balci","sequence":"first","affiliation":[]},{"given":"Gozde Merve","family":"Demirci","sequence":"additional","affiliation":[]},{"given":"Hilmi","family":"Demirhan","sequence":"additional","affiliation":[]},{"given":"Salih","family":"Sarp","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Balki, F., Demirhan, H, Sarp, S.: Neural machine translation for Turkish to English using deep learning. 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