網路社群媒體如臉書、Twitter等等常隱含著豐富的資訊，且這些資訊的議題廣泛。舉凡政治、經濟、教育、體育或人權等等，都有網路社群進行大量的討論。因此，近年來有許多研究方向是針對網路社群的活動進行討論，嘗試要藉由網路社群來進行意見觀察、分析、強化討論或做出決策。也就是說，網路社群媒體中的整體意見其實至少就代表了某個子群體的所有意見之縮影，而且因為網路傳播的力量，這樣的意見縮影也非常可能跟完整現實世界的整體意見有很大的關聯，甚至還可能反過來影響現實世界的整體意見。也因此，能夠推測出網路社群媒體中的整體意見事實上對於理解並影響整體真實世界的意見可以帶來很大的助益。
本論文旨在探討如何萃取社群媒體上，眾多的包含某特定主題的文章，進行情緒分析，以及考量使用者之間的贊同、反對情形，綜合以上考量以進行社群媒體上的關鍵詞票選分析。

Social media such as Facebook, Twitter generate lots of messages carrying news information and opinions of users on a wide range of topics. Topics can be widely related to politics, economics, education, sports, human rights, and the like.
Thus, many works have focused on social media application. They tried to monitor and analyze the opinion polarity in social media to raise the discussion frequency and even policy making. That is to say, the opinion in social media often represents a certain subgroup of overall opinion. Also, with the propagation power of Internet, the subgroup on behalf of the overall opinion is probably related to overall public opinion in real world; it can even affect the overall public opinion in real world. Consequently, it is obviously beneficial if we can infer the opinion from social media to affect the overall public opinion in real world.
This thesis aims at the research on how to extract opinions from posts containing certain topics, and how to capture the relations between comment users and the author in order to make election predictions.

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