Behnam Bahrak


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EmoPars: A Collection of 30K Emotion-Annotated Persian Social Media Texts
Nazanin Sabri | Reyhane Akhavan | Behnam Bahrak
Proceedings of the Student Research Workshop Associated with RANLP 2021

The wide reach of social media platforms, such as Twitter, have enabled many users to share their thoughts, opinions and emotions on various topics online. The ability to detect these emotions automatically would allow social scientists, as well as, businesses to better understand responses from nations and costumers. In this study we introduce a dataset of 30,000 Persian Tweets labeled with Ekman’s six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language. In this paper, we explain the data collection and labeling scheme used for the creation of this dataset. We also analyze the created dataset, showing the different features and characteristics of the data. Among other things, we investigate co-occurrence of different emotions in the dataset, and the relationship between sentiment and emotion of textual instances. The dataset is publicly available at

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UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
Alireza Salemi | Nazanin Sabri | Emad Kebriaei | Behnam Bahrak | Azadeh Shakery
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Detecting which parts of a sentence contribute to that sentence’s toxicity—rather than providing a sentence-level verdict of hatefulness— would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team’s, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity- based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition’s evaluation phase.