Augmented Deep Learning Model for Social Network Sentiment Analysis
Keywords:
Deep learning, CNN, LSTM, RCNN, Sentiment analysis, social media analysisAbstract
Users of social media communicate their views, wants, socialise, and share their opinions as the number of users grows. Resources of high quality are required for social media sentiment analysis, but there aren't many of them available for languages other than English, especially Arabic. Both the amount of the corpus and the calibre of the annotations in the Arabic resources that are readily available are lacking. In this study, we offer a Facebook-sourced Arabic sentiment analysis corpus with 60K comments that have been manually marked as a gold standard and classified as positive and negative. In order to annotate the corpus, we used self-training and remote supervision techniques. in this paper we propose a deep learning approach that allows companies and organizations to evaluate users' opinions on the quality of their services, by analysing their opinions on Facebook. We display three deep Learning models Convolutional Neural (CNN), Recurrent Convolutional (RCNN), Long Term Memory (LSTM) for Arabic sentiment analysis with the assistance of word embedding. The model accuracy was measured, and we got average accuracy of 82.1 % which was better than both CNN and RCNN. Also, applying increasing that data to the body increments LSTM accuracy by 8.7 %.
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