Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour, wKia; Gogate, Mandar; Adeel, Ahsan; Larijani, Hadi; Hussain, Amir

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.

• Journal Article Type: Article
• Acceptance Date: May 4, 2021
• Online Publication Date: May 12, 2021
• Publication Date: 2021
• Deposit Date: Jun 17, 2021
• Publicly Available Date: Jun 17, 2021
• Journal: Entropy
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 5
• Article Number: 596
• DOI: https://doi.org/10.3390/e23050596
• Keywords: sentiment analysis; deep learning; CNN; LSTM; classification
• Public URL: http://researchrepository.napier.ac.uk/Output/2781182