Sentiment analysis of persian movie reviews using deep learning

Citation

Dashtipour K, Gogate M, Adeel A, Larijani H & Hussain A (2021) Sentiment analysis of persian movie reviews using deep learning. Entropy, 23 (5), Art. No.: 596. https://doi.org/10.3390/e23050596

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.

Keywords
sentiment analysis; deep learning; CNN; LSTM; classification

Journal
Entropy: Volume 23, Issue 5

• Published version