Sentiment Analysis of Persian Movie Reviews Using Deep Learning
| Autor: | Ahsan Adeel, Kia Dashtipour, Hadi Larijani, Amir Hussain, Mandar Gogate |
|---|---|
| Přispěvatelé: | University of Stirling, Edinburgh Napier University, University of Wolverhampton, Glasgow Caledonian University (GCU) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science QC1-999 General Physics and Astronomy 02 engineering and technology Machine learning computer.software_genre Astrophysics Convolutional neural network NLP Persian Article [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] Machine Learning [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 0202 electrical engineering electronic engineering information engineering business.industry Deep learning Physics Sentiment analysis deep learning 020206 networking & telecommunications Autoencoder language.human_language Support vector machine QB460-466 classification Multilayer perceptron sentiment analysis language 020201 artificial intelligence & image processing Artificial intelligence business LSTM computer CNN Movie reviews |
| Zdroj: | Entropy Volume 23 Issue 5 Entropy, MDPI, 2021, 23 (5), pp.596. ⟨10.3390/e23050596⟩ Entropy, Vol 23, Iss 596, p 596 (2021) |
| ISSN: | 1099-4300 |
| DOI: | 10.3390/e23050596 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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