# Exploiting Deep Learning for Persian Sentiment Analysis

arXiv: https://arxiv.org/abs/1808.05077 · HTML: https://mlsanity.com/paper/1808.05077/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods](https://mlsanity.com/paper/2104.14751/) (2021)
- [A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis](https://mlsanity.com/paper/2103.02636/) (2021)
- [The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach](https://mlsanity.com/paper/2101.08087/) (2021)
- [Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets](https://mlsanity.com/paper/2212.06041/) (2022)
- [Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs](https://mlsanity.com/paper/2306.12679/) (2023)
- [Deep Learning-based Sentiment Analysis in Persian Language](https://mlsanity.com/paper/2403.11069/) (2024)

## Abstract (arXiv)

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.