# ArmanEmo: A Persian Dataset for Text-based Emotion Detection

arXiv: https://arxiv.org/abs/2207.11808 · HTML: https://mlsanity.com/paper/2207.11808/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [How transfer learning impacts linguistic knowledge in deep NLP models?](https://mlsanity.com/paper/2105.15179/) (2021)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems](https://mlsanity.com/paper/1512.08756/) (2015)
- [EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation](https://mlsanity.com/paper/1908.06264/) (2019)
- [ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT](https://mlsanity.com/paper/1904.00132/) (2019)
- [EmoTxt: A Toolkit for Emotion Recognition from Text](https://mlsanity.com/paper/1708.03892/) (2017)
- [Attention-based Modeling for Emotion Detection and Classification in Textual Conversations](https://mlsanity.com/paper/1906.07020/) (2019)

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

- [PersianLLaMA: Towards Building First Persian Large Language Model](https://mlsanity.com/paper/2312.15713/) (2023)
- [Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT](https://mlsanity.com/paper/2404.02403/) (2024)
- [TookaBERT: A Step Forward for Persian NLU](https://mlsanity.com/paper/2407.16382/) (2024)
- [Emotion Analysis of Tweets Banning Education in Afghanistan](https://mlsanity.com/paper/2306.16268/) (2023)
- [Benchmarking Open-Source Large Language Models for Persian in Zero-Shot and Few-Shot Learning](https://mlsanity.com/paper/2510.12807/) (2025)
- [Mining Mental Health Signals: A Comparative Study of Four Machine Learning Methods for Depression Detection from Social Media Posts in Sorani Kurdish](https://mlsanity.com/paper/2508.15829/) (2025)
- [Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models](https://mlsanity.com/paper/2509.05719/) (2025)
- [Can LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian](https://mlsanity.com/paper/2511.19719/) (2025)
- [The Annotation Bottleneck in Persian Text NLP: Persian as an Annotation-Scarce Language](https://mlsanity.com/paper/2608.24698/) (2026)

## Abstract (arXiv)

With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years. It has many applications, especially for businesses and online service providers, where emotion detection techniques can help them make informed commercial decisions by analyzing customers/users' feelings towards their products and services. In this study, we introduce ArmanEmo, a human-labeled emotion dataset of more than 7000 Persian sentences labeled for seven categories. The dataset has been collected from different resources, including Twitter, Instagram, and Digikala (an Iranian e-commerce company) comments. Labels are based on Ekman's six basic emotions (Anger, Fear, Happiness, Hatred, Sadness, Wonder) and another category (Other) to consider any other emotion not included in Ekman's model. Along with the dataset, we have provided several baseline models for emotion classification focusing on the state-of-the-art transformer-based language models. Our best model achieves a macro-averaged F1 score of 75.39 percent across our test dataset. Moreover, we also conduct transfer learning experiments to compare our proposed dataset's generalization against other Persian emotion datasets. Results of these experiments suggest that our dataset has superior generalizability among the existing Persian emotion datasets. ArmanEmo is publicly available for non-commercial use at https://github.com/Arman-Rayan-Sharif/arman-text-emotion.