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)

Abstract

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
JournalEntropy
Volume23
Issue number5
DOIs
Publication statusPublished - 12 May 2021

Keywords

  • 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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