Online Messages Sentiments Analysis Based on Long Short-Term Memory


  •  Yunke Zhao    

Abstract

In December of 2019, an extremely infectious and deadly pandemic ambushed China. In Wuhan, the novel coronavirus COVID-19 suddenly broke out and spread rapidly to other countries. COVID-19 became a worldwide disaster, affecting not only physical, but also emotional health on a global scale. We wanted to record this change based on the sentiment analysis model and to examine the relationship between world events and the positivity of posts on social media.
To analyze this relationship, we utilized a set of movie reviews as a training sample to construct a sentiment analysis model based on the Long Short-Term Memory neural network theory, and calculate the texts' sentiment score. We then analyzed the overall trend of the data, and discussed the reason behind the tendency. The principal result was that, as the pandemic progressed, online sentiment generally became more positive. We believe that this is because people gradually become more accustomed to life in the COVID-19 era.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-1844
  • ISSN(Online): 1913-1852
  • Started: 2007
  • Frequency: monthly

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h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

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