Abstract

LSTM can be combined with other capabilities you learned from machine learning to improve sentiment analysis. This is an important application of NLP, and, as stressed earlier, NLP is always to be viewed while considering specific applications where it comes in handy.

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.

Source: Salih Balci, Gozde Merve Demirci, Hilmi Demirhan, and Salih Sarp , https://link.springer.com/chapter/10.1007/978-3-031-11432-8_3
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