# DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus

arXiv: https://arxiv.org/abs/2004.05328 · HTML: https://mlsanity.com/paper/2004.05328/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [FaBERT: Pre-training BERT on Persian Blogs](https://mlsanity.com/paper/2402.06617/) (2024)
- [FaMTEB: Massive Text Embedding Benchmark in Persian Language](https://mlsanity.com/paper/2502.11571/) (2025)
- [TookaBERT: A Step Forward for Persian NLU](https://mlsanity.com/paper/2407.16382/) (2024)
- [Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets](https://mlsanity.com/paper/2212.06041/) (2022)
- [A Persian Benchmark for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2303.00408/) (2023)
- [Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews](https://mlsanity.com/paper/2602.12778/) (2026)
- [IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources](https://mlsanity.com/paper/2606.20089/) (2026)

## Abstract (arXiv)

This paper focuses on how to extract opinions over each Persian sentence-level text. Deep learning models provided a new way to boost the quality of the output. However, these architectures need to feed on big annotated data as well as an accurate design. To best of our knowledge, we do not merely suffer from lack of well-annotated Persian sentiment corpus, but also a novel model to classify the Persian opinions in terms of both multiple and binary classification. So in this work, first we propose two novel deep learning architectures comprises of bidirectional LSTM and CNN. They are a part of a deep hierarchy designed precisely and also able to classify sentences in both cases. Second, we suggested three data augmentation techniques for the low-resources Persian sentiment corpus. Our comprehensive experiments on three baselines and two different neural word embedding methods show that our data augmentation methods and intended models successfully address the aims of the research.