Author: Dashtipour, Kia; Gogate, Mandar; Adeel, Ahsan; Larijani, Hadi; Hussain, Amir
Title: Sentiment Analysis of Persian Movie Reviews Using Deep Learning Cord-id: b3w3hc0l Document date: 2021_5_12
ID: b3w3hc0l
Snippet: 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
Document: 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.
Search related documents:
Co phrase search for related documents- ablation study and machine learning: 1, 2
- ablation study and machine learning approach: 1
- accuracy achieve and activation function: 1
- accuracy achieve and activation function layer: 1
- accuracy achieve and logistic regression: 1, 2, 3, 4, 5, 6
- accuracy achieve and logistic regression svm: 1
- accuracy achieve and long lstm short term memory: 1, 2, 3
- accuracy achieve and long short term: 1, 2, 3, 4, 5, 6
- accuracy achieve and long short term memory: 1, 2, 3, 4, 5, 6
- accuracy achieve and loss function: 1, 2
- accuracy achieve and low accuracy: 1, 2
- accuracy achieve and low accuracy achieve: 1, 2
- accuracy achieve and low performance: 1
Co phrase search for related documents, hyperlinks ordered by date