# A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods

arXiv: https://arxiv.org/abs/2104.14751 · HTML: https://mlsanity.com/paper/2104.14751/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 74 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data](https://mlsanity.com/paper/1806.04511/) (2018)
- [SentiPers: A Sentiment Analysis Corpus for Persian](https://mlsanity.com/paper/1801.07737/) (2018)
- [A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks](https://mlsanity.com/paper/1909.13568/) (2019)
- [Exploiting Deep Learning for Persian Sentiment Analysis](https://mlsanity.com/paper/1808.05077/) (2018)
- [LexiPers: An ontology based sentiment lexicon for Persian](https://mlsanity.com/paper/1911.05263/) (2019)
- [A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach](https://mlsanity.com/paper/2008.03020/) (2020)
- [Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method](https://mlsanity.com/paper/1412.8079/) (2014)
- [A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis](https://mlsanity.com/paper/2103.02636/) (2021)

## Built on by (most cited first, 8 of 8)

- [MELAC: Massive Evaluation of Large Language Models with Alignment of Culture in Persian Language](https://mlsanity.com/paper/2508.00673/) (2025)
- [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)
- [FarSSiBERT: A Novel Transformer-based Model for Semantic Similarity Measurement of Persian Social Networks Informal Texts](https://mlsanity.com/paper/2407.19173/) (2024)
- [Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging](https://mlsanity.com/paper/2310.00572/) (2023)
- [Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models](https://mlsanity.com/paper/2509.05719/) (2025)
- [Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews](https://mlsanity.com/paper/2602.12778/) (2026)
- [Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts](https://mlsanity.com/paper/2602.21214/) (2026)

## Abstract (arXiv)

Social media has been remarkably grown during the past few years. Nowadays, posting messages on social media websites has become one of the most popular Internet activities. The vast amount of user-generated content has made social media the most extensive data source of public opinion. Sentiment analysis is one of the techniques used to analyze user-generated data. The Persian language has specific features and thereby requires unique methods and models to be adopted for sentiment analysis, which are different from those in English language. Sentiment analysis in each language has specified prerequisites; hence, the direct use of methods, tools, and resources developed for English language in Persian has its limitations. The main target of this paper is to provide a comprehensive literature survey for state-of-the-art advances in Persian sentiment analysis. In this regard, the present study aims to investigate and compare the previous sentiment analysis studies on Persian texts and describe contributions presented in articles published in the last decade. First, the levels, approaches, and tasks for sentiment analysis are described. Then, a detailed survey of the sentiment analysis methods used for Persian texts is presented, and previous relevant works on Persian Language are discussed. Moreover, we present in this survey the authentic and published standard sentiment analysis resources and advances that have been done for Persian sentiment analysis. Finally, according to the state-of-the-art development of English sentiment analysis, some issues and challenges not being addressed in Persian texts are listed, and some guidelines and trends are provided for future research on Persian texts. The paper provides information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete sentiment analysis system.