A semantic sentiment recognition model based on ontology and cellular deep learning automata
Subject Areas : Special
Hoshang Salehi
1
,
Reza Ghaemi
2
*
,
maryam khairabadi
3
1 - 1 Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabor, Iran
2 - Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran
3 - Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabor, Iran
Keywords: Opinion mining, Sentiment analysis, Deep neural network, Cellular automata, Ontology.,
Abstract :
Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to the news of the day, current events, etc., and recognizing these feelings faces many challenges. So far, various methods such as machine learning, statistical approaches, artificial intelligence, etc., have been proposed for the purpose of detecting emotions, which despite their many applications; But they have not yet been able to have acceptable accuracy, transparency and accuracy. Therefore, in this article, an ontology-based semantic analysis model using cellular deep learning automata based on GMDH deep neural network is presented. Ontology approach is used to select salient features based on production rules and cellular deep learning automata is used to classify user sentiments. The main innovation of this article is the proposed algorithm that a deep learning method is developed to process only one expression and then by transferring it to the field of cellular automata, parallel or distributed processing is provided. In this article, the data sets of Amazon customers, Twitter, Facebook, fake news of COVID-19, Amazon and fake news network are used. By simulating the proposed method, it was observed that the proposed method has an average improvement of 3% compared to other methods
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