Sentiment Analysis Using State of the Art Machine Learning Techniques

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Balci S., Demirci G. M., Demirhan H., Sarp S.

9th Machine Intelligence and Digital Interaction Conference, MIDI 2021, Virtual, Online, 9 - 10 December 2021, vol.440 LNNS, pp.34-42 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 440 LNNS
  • Doi Number: 10.1007/978-3-031-11432-8_3
  • City: Virtual, Online
  • Page Numbers: pp.34-42
  • Keywords: Sentiment analysis, Bag of tricks, Transformer, BERT, CNN
  • Kocaeli University Affiliated: No

Abstract

© 2022, The Author(s).Sentiment analysis is one of the essential and challenging tasks in the Artificial Intelligence field due to the complexity of the languages. 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.