Mohammad Arif Payenda

• Hussiny, Mohammad Ali; Payenda, Mohammad Arif & Øvrelid, Lilja (2024). PersianEmo: Enhancing Farsi-Dari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model. Proceedings of LREC. p. 257–263. Show summary

  Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise models and larger datasets specifically focusing on the Farsi and Dari dialects. In this research, we introduce “LearnArmanEmo” as a new dataset and a superior ensemble approach for Persian text emotion classification. Our proposed model, which combines XLM-RoBERTa-large and BiGRU, undergoes evaluation on LetHerLearn for the Dari dialect, ARMANEMO for the Farsi dialect, and LearnArmanEmo for both Dari and Farsi dialects. The empirical results substantiate the efficacy of our approach with the combined model demonstrating superior performance. Specifically, our model achieves an F1 score of 72.9% on LetHerLearn, an F1 score of 77.1% on ARMANEMO, and an F1 score of 78.8% on the LearnArmanEmo dataset, establishing it as a better ensemble model for these datasets. These findings underscore the potential of this hybrid model as a useful tool for enhancing the performance of emotion analysis in Persian language processing.

• Hussiny, Mohammad Ali; Payenda, Mohammad Arif & Vahidi, Abdul Razaq (2023). SentiDariPers: Sentiment Analysis of Dari-Persian Tweets Based on People’s Views and Opinion. In Valencia-Garcia, Rafael; Bucaram-Leverone, Martha; Del Cioppo-Morstadt, Javier; Vera-Lucio, Nestor & Centanaro-Quiroz, Pablo Humberto (Ed.), International Conference on Technologies and Innovation (CITI 2023). Springer. ISSN 978-3-031-45681-7. p. 138–156. doi: 10.1007/978-3-031-45682-4_11. Show summary

  In the research area of sentiment analysis, there is a noticeable gap when it comes to the Dari-Persian dialect. To bridge this gap, our research aimed to curate a comprehensive dataset encompassing people’s opinions in this specific language variant. This paper presents the development of a benchmark sentiment annotated dataset for the Dari dialect of Persian, which serves as an official language of Afghanistan. The dataset, named “SentiDariPers”, comprises 43,089 tweets posted between August 2021 and April 2023. It has been manually annotated with four sentiment classes: Negative, Positive, Neutral, and Mixed. We applied a range of models, such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). Additionally, we develop an ensemble model that combines different sets of sentiment classes for each system. We present a detailed comparative analysis of the results obtained from these models. Experimental findings demonstrate that the ensemble model achieves the highest accuracy 91%. We provide insights into the data collection and annotation process, offer relevant dataset statistics, discuss the experimental results, and provide further analysis of the data.

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