TY - JFULL AU - Pingping Lin and Xudong Luo and Yifan Fan PY - 2020/1/ TI - A Survey of Sentiment Analysis Based on Deep Learning T2 - International Journal of Computer and Information Engineering SP - 472 EP - 485 VL - 14 SN - 1307-6892 UR - https://publications.waset.org/pdf/10011630 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 168, 2020 N2 - Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis. ER -