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Exploiting Deep Learning for Persian Sentiment Analysis

Dashtipour, K.; Gogate, M.; Adeel, A.; Ieracitano, C.; Larijani, H.; Hussain, A.


K. Dashtipour

A. Adeel

C. Ieracitano

H. Larijani


The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis. However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian. In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset. The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model. Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP.

Presentation Conference Type Conference Paper (Published)
Conference Name 9th International Conference, BICS 2018
Start Date Jul 7, 2018
End Date Jul 8, 2018
Online Publication Date Oct 6, 2018
Publication Date 2018
Deposit Date Sep 27, 2019
Publisher Springer
Pages 597-604
Series Title Lecture Notes in Computer Science
Series Number 10989
Series ISSN 0302-9743
Book Title Advances in Brain Inspired Cognitive Systems
Keywords Persian sentiment analysis, Persian movie reviews, Deep learning
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