微型部落格（Twitter或Facebook）近年來在互聯網用戶中已成為非常流行的通訊工具。在這些微型部落格上有很多的使用者分享他們對生活中各個主題的想法以及意見。訊息藉由一個人透過電腦或是手機來產生以及管理, 然而大部分的訊息都是文字的訊息. 那這些意見是可以幫助其他使用者參考以及幫助他們做決定. 所以如果可以幫助使用者去收集以及分析這些資料, 這是一件很有意義的事. 然而這件事情是非常具有挑戰性, 因為在微型部落格上的訊息通常是很短的而且非常口語的, 傳統意見探勘的演算法在處理這類型的資料並沒有辦法得到很好的效能. 因此在這篇論文中, 我們提出一個可以自動幫助分析語意的系統架構. 我們使用手動標記過的Twitter資料. 那Twitter是一個很受歡迎的微型部落格. 在此系統中機器學習的演算法會自動的取出訊息中含有意見的把一些不含意見的訊息給過濾掉接著判斷這些含有意見的訊息的語意方向(正面的, 負面的). 最後的實驗結果顯示我們的系統在處理微型部落格語意分析應用的方面得到很好的結果.

Microblogging (Twitter or Facebook) has become a very popular communication tool among Internet users in recent years. Information is generated and managed through either computer or mobile devices by one person and is consumed by many other persons, with most of this user-generated content being textual information. As there are a lot of raw data of people posting real time messages about their opinions on a variety of topics in daily life, it is a worthwhile research endeavor to collect and analyze these data, which may be useful for users or managers to make informed decisions, for example. However this problem is challenging because a micro-blog post is usually very short and colloquial, and traditional opinion mining algorithms do not work well in such type of text. Therefore, in this paper, we propose a new system architecture that can automatically analyze the sentiments of these messages. We combine this system with manually annotated data from Twitter, one of the most popular microblogging platforms, for the task of sentiment analysis. In this system, machines can learn how to automatically extract the set of messages which contain opinions, filter out non- opinion messages and determine their sentiment directions (i.e. positive, negative). Experimental results verify the effectiveness of our system on sentiment analysis in real microblogging applications.

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