ArticleEntropy (Basel, Switzerland)2021
Sentiment Analysis of Persian Movie Reviews Using Deep Learning.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A novel lexicon dictionary and CNN-LSTM employed hybrid approach for sentiment detection of COVID-19 vaccines.Scientific reports · 2026Article
- Will there be new trends in the public's attention to express services in the post-COVID-19 era?PloS one · 2026Article
- Sentiment analysis using long short term memory and amended dwarf mongoose optimization algorithm.Scientific reports · 2025Article
- DenseHillNet: a lightweight CNN for accurate classification of natural images.PeerJ. Computer science · 2024Article
- Position-Wise Gated Res2Net-Based Convolutional Network with Selective Fusing for Sentiment Analysis.Entropy (Basel, Switzerland) · 2023Article
- What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets.PeerJ. Computer science · 2023Article
- Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems.PeerJ. Computer science · 2023Article
- Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique.Foods (Basel, Switzerland) · 2022Article
- Text Sentiment Analysis Based on a New Hybrid Network Model.Computational intelligence and neuroscience · 2022Article
- An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy.Entropy (Basel, Switzerland) · 2021Article
- Improving Perceived Quality of Live Adaptative Video Streaming.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.