Dashtipour K, Gogate M, Adeel A, Larijani H, Hussain A. Sentiment Analysis of Persian Movie Reviews Using Deep Learning.
ENTROPY (BASEL, SWITZERLAND) 2021;
23:596. [PMID:
34066133 PMCID:
PMC8151596 DOI:
10.3390/e23050596]
[Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 02/07/2023]
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.
Collapse