Public Opinion Toward CSSTA: A Text Mining Approach

Yi-An Wu ； Shu-Kai Hsieh

《中文計算語言學期刊》 19卷4期 (2014/12) Pp. 19-28

並列摘要

Extracting policy positions from the texts of social media becomes an important technique since instant responses of political news from the public can be revealed, and also one can predict the electoral behavior from this information. The recent highly-debated Cross-Strait Service Trade Agreement (CSSTA) provides large amounts of texts, giving us an opportunity to test people's stance by the text mining method. We use the keywords of each position to do the binary classification of the texts and count the score of how positive or negative attitudes toward CSSTA. We further do the trend analysis to show how the supporting rate fluctuates according to the events. This approach saves human labor of the traditional content analysis and increases the objectivity of the judgement standard.

並列關鍵字

Policy Position ； Opinion Mining ； Politics ； Social Media ； Trend Analysis

參考文獻

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