# Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches

arXiv: https://arxiv.org/abs/2211.08029 · HTML: https://mlsanity.com/paper/2211.08029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 73 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [End-to-End Multimodal Emotion Recognition using Deep Neural Networks](https://mlsanity.com/paper/1704.08619/) (2017)
- [Data Augmentation Using GANs](https://mlsanity.com/paper/1904.09135/) (2019)
- [AutoBalance: Optimized Loss Functions for Imbalanced Data](https://mlsanity.com/paper/2201.01212/) (2022)
- [How transfer learning impacts linguistic knowledge in deep NLP models?](https://mlsanity.com/paper/2105.15179/) (2021)
- [Cross-lingual and Multilingual Speech Emotion Recognition on English and French](https://mlsanity.com/paper/1803.00357/) (2018)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [Sarcasm Detection: A Comparative Study](https://mlsanity.com/paper/2107.02276/) (2021)
- [Hierarchical Transformer Network for Utterance-level Emotion Recognition](https://mlsanity.com/paper/2002.07551/) (2020)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT](https://mlsanity.com/paper/2404.02403/) (2024)
- [From Words and Exercises to Wellness: Farsi Chatbot for Self-Attachment Technique](https://mlsanity.com/paper/2310.09362/) (2023)

## Abstract (arXiv)

Emotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces. Detecting emotion can help us in different fields, including opinion mining. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms is informal, making the emotion detection task difficult. EmoPars and ArmanEmo are two new human-labeled emotion datasets for the Persian language. These datasets, especially EmoPars, are suffering from inequality between several samples between two classes. In this paper, we evaluate EmoPars and compare them with ArmanEmo. Throughout this analysis, we use data augmentation techniques, data re-sampling, and class-weights with Transformer-based Pretrained Language Models(PLMs) to handle the imbalance problem of these datasets. Moreover, feature selection is used to enhance the models' performance by emphasizing the text's specific features. In addition, we provide a new policy for selecting data from EmoPars, which selects the high-confidence samples; as a result, the model does not see samples that do not have specific emotion during training. Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and EmoPars, respectively, which are new state-of-the-art results in these benchmarks.