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   investigating shallow and deep learning techniques for emotion classification in short persian texts  
   
نویسنده rasouli mahdi ,kiani vahid
منبع journal of ai and data mining - 2023 - دوره : 11 - شماره : 4 - صفحه:587 -598
چکیده    The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. this paper presents a comprehensive exploration of shallow and deep learning methods for emotion detection in short persian texts. shallow learning methods employ feature extraction and dimension reduction to enhance classification accuracy. on the other hand, deep learning methods utilize transfer learning and word embedding, particularly bert, to achieve high classification accuracy. a persian dataset called &shortpersianemo& is introduced to evaluate the proposed methods, comprising 5472 diverse short persian texts labeled in five main emotion classes. the evaluation results demonstrate that transfer learning and bert-based text embedding perform better in accurately classifying short persian texts than alternative approaches. the dataset of this study shortpersianemo will be publicly available online at https://github.com/vkiani/shortpersianemo.
کلیدواژه natural language processing ,emotion classification ,persian text ,emotion detection benchmark ,deep learning
آدرس university of bojnord, department of computer engineering, iran, university of bojnord, department of computer engineering, iran
پست الکترونیکی v.kiani@ub.ac.ir
 
     
   
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