ArmanEmo: a Persian dataset for text-based emotion detection.
The rapid growth of textual data on social media platforms has increased attention to emotion detection (ED) from text. Businesses and online service providers leverage ED techniques to analyze customer or user sentiments toward their products and services, enabling more informed and strategic decis...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2565 - 2588 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909076&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909076 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909076 10.1007/s10579-025-09817-4 ppf: 2565 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.4MB tig: atl: ArmanEmo: a Persian dataset for text-based emotion detection. aug: au: Mirzaee, Hossein Peymanfard, Javad Habibzadeh Moshtaghin, Hamid Zeinali, Hossein affil: https://ror.org/04gzbav43 Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran https://ror.org/01jw2p796 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran https://ror.org/02cc4gc68 Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran https://ror.org/04gzbav43 Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran su: Emotion recognition Emotions Transformer models Sentiment analysis Transfer of training Content analysis Social media sug: subj: Emotion recognition Emotions Transformer models Sentiment analysis Transfer of training Content analysis Social media keyword: BERT model Ekman's model Emotion detection Persian dataset Psychology and Cognitive Sciences Psychology ab: The rapid growth of textual data on social media platforms has increased attention to emotion detection (ED) from text. Businesses and online service providers leverage ED techniques to analyze customer or user sentiments toward their products and services, enabling more informed and strategic decision-making. 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 comments. The labels are based on Ekman's six basic emotions-Anger, Fear, Happiness, Hatred, Sadness, and Wonder-augmented by an additional category, "Other", to account for emotions outside Ekman's model. In addition to 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% across our test dataset. Moreover, we also conduct transfer learning experiments to evaluate how well models trained on our proposed dataset perform on unseen samples from another Persian emotion dataset, EmoPars. Results of these experiments suggest that the model trained on our training set has a superior performance to the model trained on the training set of EmoPars, even when the models are evaluated on the test set of EmoPars. ArmanEmo is publicly available for non-commercial use at https://github.com/Arman-Rayan-Sharif/arman-text-emotion. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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