This project describes an approach to analyze public sentiments with social media data and provides an example of the Twitter discourse during the 2019 Chinese National Day. The objective is to study the online discourse towards China with NLP algorithms,as well as observe the temporal, spatial and lingual characteristics of the expressed sentiments. Firstly, the Twitter data sets were collected between Sept 30 and Oct 3 through API and part of them were manually labeled to train the SVM. Then, a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets. After that, the tweets sentiments' time fluctuation, spatial distribution and frequently used words were given. Finally, we conclude by highlighting the possible consequences of the overall negative image of China in the English-speaking discourses and indicating future directions .
As the Internet gradually penetrates into people's daily lives and empowers everyone to demonstrate and exchange opinions and sentiments online, individual citizens are increasingly participating in the agenda-setting of public affairs and the design and implementation of official policies. The current study describes an approach to analyze online public sentiments using social media data and provides an example of Twitter discourse during the 2019 Chinese National Day. Over 300,000 tweets were collected between Sept 30 and Oct 3 , and a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets. This method avoids complex structures while yielding an average accuracy of over 96\% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the expressed sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in different directions. The sentiment of tweets also diverges from nation to nation, but is generally consistent with the country's official relations with China to varying degrees. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. At last, possible directions for further studies are indicated .
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As the Internet gradually penetrates into people's daily lives and empowers everyone to demonstrate and exchange opinions and sentiments online, individual citizens are increasingly participating in the agenda-setting of public affairs and the design and implementation of official policies. The current study
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The objective is to study the online discourse towards China with NLP algorithms,as well as observe the temporal, spatial and lingual characteristics of the expressed sentiments. Firstly, the Twitter data sets
Over 300,000 tweets
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through API and part of them were manually labeled to train the SVM. Then,
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After that, the tweets sentiments' time fluctuation, spatial distribution and frequently used words were given. Finally, we conclude by highlighting the possible consequences of the overall negative image of China in the English-speaking discourses and indicating future directions
This method avoids complex structures while yielding an average accuracy of over 96\% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the expressed sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in different directions. The sentiment of tweets also diverges from nation to nation, but is generally consistent with the country's official relations with China to varying degrees. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. At last, possible directions for further studies are indicated