Public Opinion Toward CSSTA: A Text Mining Approach

Public Opinion Toward CSSTA: A Text Mining Approach

Recently, the public of Taiwan has had a heated debate on the issue of Cross-Strait Service Trade Agreement (CSSTA). After months of simmering tensions between ruling party and opposition party strongly backed by the student-led Sunflower Movement, the debate has finally reached a breaking point on March 18, 2014, at which students occupied the Legislative Yuan. During this period, novel communication such as Facebook sharing, instant messaging, and discussions on PTT have reshaped the social movement since they are easily accessible and instantly responded. The social media has become the dominant source in opinion shaping and the accompanying sentiment spread. The extraction and tracking of uprising political opinions and events such as CSSTA has become one of the most important topics that receive much attention. With the huge amounts of texts, it is not possible to analyze and interpreting the social and political texts manually. Instead, we propose to use the text mining approach, which automatically extract opinion and information profiles from the texts. Moreover, this approach also strengthens the objectivity, for the norms are set a priori, and thus human biases are reduced.