# A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks

arXiv: https://arxiv.org/abs/1909.13568 · HTML: https://mlsanity.com/paper/1909.13568/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- [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)
- [aiSTROM -- A roadmap for developing a successful AI strategy](https://mlsanity.com/paper/2107.06071/) (2021)
- [Classification of retail products: From probabilistic ranking to neural networks](https://mlsanity.com/paper/2312.07482/) (2023)
- [Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models](https://mlsanity.com/paper/2501.04828/) (2025)
- [Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced GPU Demands: A Case Study in Dementia Identification](https://mlsanity.com/paper/2504.12494/) (2025)
- [Exa-PSD: a new Persian sentiment analysis dataset on Twitter](https://mlsanity.com/paper/2602.20892/) (2026)

## Abstract (arXiv)

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10-15% and 3-4% respectively, using benchmark Persian product and hotel reviews corpora.