Sentiment analysis of Persian movie reviews using deep learning

Kia Dashtipour*, Mandar Gogate, Ahsan Adeel, Hadi Larijani, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
2 Downloads (Pure)


Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

Original languageEnglish
Article number596
Number of pages16
Issue number5
Publication statusPublished - 12 May 2021


  • sentiment analysis
  • deep learning
  • CNN
  • LSTM
  • classification

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering


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