Sentiment Classification Using Recurrent Neural Network

Author(s):  
Kavita Moholkar ◽  
Krupa Rathod ◽  
Krishna Rathod ◽  
Mritunjay Tomar ◽  
Shashwat Rai
2019 ◽  
Vol 502 ◽  
pp. 268-278 ◽  
Author(s):  
Chaotao Chen ◽  
Run Zhuo ◽  
Jiangtao Ren

Author(s):  
Hamed Jelodar ◽  
Yongli Wang ◽  
Rita Orji ◽  
Hucheng Huang

AbstractInternet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19–related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.


2016 ◽  
Vol 25 (4) ◽  
pp. 601-607 ◽  
Author(s):  
Yangsen Zhang ◽  
Yixuan Tong ◽  
Yuru Jiang

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