Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning | ||
| Journal of AI and Data Mining | ||
| دوره 9، شماره 2، تیر 2021، صفحه 169-179 اصل مقاله (1.69 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.9992.2136 | ||
| نویسندگان | ||
| Seyedeh S. Sadeghi1؛ H. Khotanlou* 2؛ M. Rasekh Mahand1 | ||
| 1Faculty of language and humanities, Bu Ali Sina University, Hamedan, Iran. | ||
| 2Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran. | ||
| چکیده | ||
| 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 23,000 Persian documents by the average length of 24 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 Word2Vec 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. 10-fold cross validation has been used to evaluate the performance of the proposed system. Experimental results show the proposed system achieved the accuracy of 97%. 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. | ||
| کلیدواژهها | ||
| Emotion Detection؛ Cognitive Linguistics؛ Deep learning؛ Learning Machines؛ cross-validation | ||
| مراجع | ||
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