Exploiting Deep Learning for Persian Sentiment Analysis
This work addresses sentiment analysis for Persian speakers, but it is incremental as it applies existing deep learning methods to a new language and dataset.
The authors tackled sentiment analysis for Persian language by developing deep autoencoders and CNNs, applied to a novel Persian movie reviews dataset, and demonstrated enhanced performance over a state-of-the-art shallow MLP model.
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.