文章基本信息

标题：The application of Deep Learning in Persian Documents Sentiment Analysis
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作者：Mohammad Bagher Dastgheib ; Sara Koleini ; Farzad Rasti 等
期刊名称：INTERNATIONAL JOURNAL OF INFORMATION SCIENCE AND MANAGEMENT
印刷版ISSN：2008-8302
电子版ISSN：2008-8310
出版年度：2020
卷号：18
期号：1
页码：1-15
语种：English
出版社：REGIONAL INFORMATION CENTER FOR SCIENCE AND TECHNOLOGY
摘要：pNowadays the amount of textual information on the web is grown rapidly. The huge textual data needs more accurate classification algorithms. Sentiment analysis is a branch of text classification that is used to classify user opinions in case of market decisions, product evaluations or measuring consumer confidence. With the rise of the production rate of Persian text data in a commercial area, improvement of the efficiency of algorithms in Persian is a must. The structure of the Persian language such as word and sentence structures poses some challenges in this area. Deep learning algorithms are recently used in NLP and especially sentiment text classification for many dominant languages like Persian. The goal is to improve the performance of classification using deep learning issues. In this work, the authors proposed a hybrid method by a combination of structural correspondence learning (SCL) and convolutional neural network (CNN). The SCL method selects the most effective pivot features so the adaptation from one domain to similar ones cannot drop the efficiency drastically. The results showed that the proposed hybrid method that is learned from one domain can act efficiently in a similar domain. The result showed that applying a combination of SCL+CNN can improve the result of sentiment classification for two domains more than 10 percent.