Mendeley
TY  _  JOUR
ID  -  13990222237125
TI  -  Convolutional Neural Networks for Sentiment Analysis in Persian Social Media
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Rohanian M.
AU  -  Salehi Mostafa
AU  -  Darzi A.
AU  -  Ranjbar Vahid
AD  -  دانشگاه تهران
AD  -  دانشگاه تهران
AD  -  دانشگاه تهران
AD  -  دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران
Y1  -  2020
PY  -  2020
VL  -  1
IS  -  
SP  -  59
EP  -  66
KW  -  Sentiment analysis‎‎social media‎convolutional neural network‎‎
KW  -  sentiment intensity‎ short texts
DO  -  
N2  -  With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entities in a given document. In this paper we propose a sentiment analysis method for Persian text using Convolutional Neural Network (CNN), a feedforward Artificial Neural Network, that categorize sentences into two and five classes (considering their intensity) by applying a layer of convolution over input data through different filters. We evaluated the method on three different datasets of Persian social media texts using Area under Curve metric. The final results show the advantage of using CNN over earlier attempts at developing traditional machine learning methods for Persian texts sentiment classification especially for short texts.
UR  - rimag.ir/en/Article/28898
L1  - rimag.ir/en/Article/Download/28898
ER  -  
