現在是網路發達的時代，很多人會在網路上發表自己心情狀態的言論，本論文的重點在透過找出網路文章中的負面事件來預測網路文章的憂鬱傾向。本研究提出事件導向負面情緒模型(Event-Driven Negative Emotion Model)方法用以判斷網路使用者是否具有憂鬱情緒。我們從台大知名BBS站(PTT)的憂鬱症板(Prozac)收集該板使用者的文章，用以觀察、訓練與實驗，並且利用中央研究院CKIP斷詞系統做文章斷詞處理。同時我們使用王壽年在2011年提出的四個詞典，經由專家篩選後整合成新的詞典，為負面事件、負面情緒、症狀和負面想法辭典。四個詞典被運用在事件導向負面情緒模型的計算中，每一篇文章皆會得到一個情緒分數(Emotion Score)，這些情緒分數代表網路使用者在寫這篇文章時的憂鬱傾向指數。我們將文章經心理學家標記是否有憂鬱傾向，當成判斷的依據，實驗結果顯示事件導向負面情緒模型(EDNE Model)的F-measure值為0.624比王壽年2011提的多重消極因素分析模型(MPFA Model)的F-measure值0.539還來的高，實驗結果證明本研究提出的方法可以利用找出該篇文章的主要負面事件詞，配合症狀與負面想法詞來判斷這篇章是否有憂鬱傾向。

Many people write their mood state on the Internet now.The focus of this paper is predict the Web user’s depression tendency by identifying negative event in the article. In this work, Event-Driven Negative Emotion Model method used to determine the Web users whether they have depression. We collected some user's articles from well-known BBS stations (PTT), its used to observe, train and experiment. The articles were segmented using Chinese Word Segmentation System with Unknown Word Extraction and POS Tagging from ACADEMIA SINICA.
In addition, we use the four Wang's 2011 dictionary, and through expert screening integrated into the new dictionary, negative events, negative emotions, symptoms and negative thoughts dictionary. The Four dictionaries were used to calculate emotion score by using Event-Driven Negative Emotion Model, these emotion scores on behalf of the depression index of Web users. The experimental results showed the F-measure value by using EDNE Model was better than using MPFA Model. So we get a conclusion that identify the major negative event term to determine whether depression is helpful.

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