Exploiting Deep Learning for Persian Sentiment Analysis
Abstract
The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis. However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian. In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset. The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model. Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP.
Cite
@article{arxiv.1808.05077,
title = {Exploiting Deep Learning for Persian Sentiment Analysis},
author = {Kia Dashtipour and Mandar Gogate and Ahsan Adeel and Cosimo Ieracitano and Hadi Larijani and Amir Hussain},
journal= {arXiv preprint arXiv:1808.05077},
year = {2018}
}
Comments
To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018)