نمایش مختصر رکورد

dc.contributor.authorLazemi, Soghraen_US
dc.contributor.authorEbrahimpour-Komleh, Hosseinen_US
dc.date.accessioned1399-07-08T18:30:13Zfa_IR
dc.date.accessioned2020-09-29T18:30:13Z
dc.date.available1399-07-08T18:30:13Zfa_IR
dc.date.available2020-09-29T18:30:13Z
dc.date.issued2018-04-01en_US
dc.date.issued1397-01-12fa_IR
dc.date.submitted2018-10-02en_US
dc.date.submitted1397-07-10fa_IR
dc.identifier.citationLazemi, Soghra, Ebrahimpour-Komleh, Hossein. (2018). Multi-Emotion Extraction from Text Using Deep Learning. International Journal of Web Research, 1(1), 62-67. doi: 10.22133/ijwr.2018.70577en_US
dc.identifier.issn2645-4335
dc.identifier.issn2645-4343
dc.identifier.urihttps://dx.doi.org/10.22133/ijwr.2018.70577
dc.identifier.urihttp://ijwr.usc.ac.ir/article_70577.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/45556
dc.description.abstractEmotions are a part of everyday communications of people and one of the important elements of human nature. We can distinguish a person's emotions from some outcome behaviors such as speech, facial expression, body movements, and gestures. Another outcome behavior is his/her grammar and written method that reflects the inner states of the person. Since people are nowadays more likely to use textual tools to make the connection, emotion extraction from the text has attracted much attention. The majority of methods in this regard consider emotion extraction from the text as a classification problem. Therefore, most studies depend on a huge number of handcrafted features and are done on feature engineering to enhance the classification performance. Considering that a text may include more than one emotion that only one of them is text dominant emotion, we model the emotion extraction problem as a multi-label classification problem by removing the fixed boundaries of emotions. Next, we recognize all the existing emotions in the sentence and in dominant emotion. Our goal is to achieve a better performance only with minimal feature engineering. To this end, we propose a hybrid deep learning model that benefits both CNN and RNN deep models. The experiments are done on a multi-label dataset including 629 sentences with eight emotional categories. Based on the results, our proposed method shows a better performance (about 0.12%) compared with available multi-label learning methods (e.g., BR, RAKEL, and MLkNN).en_US
dc.format.extent564
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Science and Cultureen_US
dc.relation.ispartofInternational Journal of Web Researchen_US
dc.relation.isversionofhttps://dx.doi.org/10.22133/ijwr.2018.70577
dc.subjectEmotion Extractionen_US
dc.subjectMulti-Label Classificationen_US
dc.subjectMachine Learningen_US
dc.subjectstructural and semantic informationen_US
dc.subjectDeep Learningen_US
dc.subjectNatural Language Processingen_US
dc.subjectHuman Computer Interactionen_US
dc.subjectSemantic Weben_US
dc.titleMulti-Emotion Extraction from Text Using Deep Learningen_US
dc.typeTexten_US
dc.contributor.departmentDepartment of Computer Engineering The University of Kashan, Kashan, Iranen_US
dc.contributor.departmentDepartment of Computer Engineering The University of Kashan, Kashan, Iranen_US
dc.citation.volume1
dc.citation.issue1
dc.citation.spage62
dc.citation.epage67


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