選舉預測在近幾年來是個熱門的研究議題。然而，在過去的研究裡，預測勝敗選的方法都是只有單純統計tweets提到候選人的篇數或是計算tweets提到候選人的正負面篇數比例。候選人的輸或贏是由很多原因造成的，例如這位候選人有發生醜聞、賄選、社會議題或是政見等等。因此在這篇論文中，我們提出一個新的觀點，想要從與候選人有關的議題事件來預測選舉結果。希望能夠找出選舉預測規則如下: 對一個候選人來講，在選舉前只要有這一連串的事件發生 “(大事件, 正面影響) → (小事件, 負面影響) → (大事件, 正面影響)”，那這位候選人就會贏得此次選舉。我們提出四種產生事件序列的方法並且利用關聯式分類模型來預測選舉結果。實驗結果顯示我們提出的方法有不錯的表現。在大部分的情況下，我們的預測準確率都超過80%

Election prediction has been studied in the recent years. However, the previous works focus on counting the number of tweets mentioning candidates to predict the election result. Many reasons cause candidates to win or lose in an election, such as political opinions, social issues, scandals and other reasons. In this paper, we consider a novel viewpoint to predict election results. For a candidate, if the following event sequence happened, “(big event, positive) → (small event, negative) → (big event, positive)”, this candidate will win the election. We consider four approaches to generate the above sequences and then apply the rule-based classifier for predict the election results. A series of experiments are performed to evaluate our approaches and the experiment results reveal that the accuracy of our approaches on predicting election results is over 80% in most of the cases.

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