Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 9، شماره: 2
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 347
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شناسه ملی سند علمی:
JR_JADM-9-2_004
تاریخ نمایه سازی: 20 مرداد 1400
چکیده مقاله:
In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination of cognitive features and deep neural network, Gated Recurrent Unit has been proposed in this paper. Five basic emotions used in this approach are: anger, happiness, sadness, surprise and fear. A total of ۲۳,۰۰۰ Persian documents by the average length of ۲۴ have been labeled for this research. Emotional constructions, emotional keywords, and emotional POS are the basic cognitive features used in this approach. On the other hand, after preprocessing the texts, words of normalized text have been embedded by Word۲Vec technique. Then, a deep learning approach has been done based on this embedded data. Finally, classification algorithms such as Naïve Bayes, decision tree, and support vector machines were used to classify emotions based on concatenation of defined cognitive features, and deep learning features. ۱۰-fold cross validation has been used to evaluate the performance of the proposed system. Experimental results show the proposed system achieved the accuracy of ۹۷%. Result of proposed system shows the improvement of several percent’s in comparison by other results achieved GRU and cognitive features in isolation. At the end, studying other statistical features and improving these cognitive features in more details can affect the results.
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نویسندگان
Seyedeh S. Sadeghi
Faculty of language and humanities, Bu Ali Sina University, Hamedan, Iran.
H. Khotanlou
Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran.
M. Rasekh Mahand
Faculty of language and humanities, Bu Ali Sina University, Hamedan, Iran.
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