今日，由於社群媒體的優點使得大眾的意見數量呈現爆炸性的成長，隨之而來的情緒分析研究受到眾人的矚目。目前，研究情緒分析主要區分成兩種方法，以字典為基礎的方法及透過標定的資料來建立模型，然後第二種方法面臨了困難點便是需要收集大量人工標定的資料。第一種方法則需要透過已存在的情緒字典來決定意見的好壞。 雖然目前有許多公認適合的字典可供使用，但是並不存在單一的字典可以涵蓋所有領域字詞的意義，因此建立一本專屬領域的情緒字典成為一個重要的工作。
在我們的研究中，我們提出了一個建立專屬領域情緒字典的方法。首先，我們在一個無任何人工標定的資料集中計算字與字之間的語意相似度，接下來將字與字的關係建立成文字關聯圖，最後透過以圖為基礎的半監督式傳播方法將事先標定的值傳播到詞性未知的字。我們使用推特的資料集建立金融領域情緒字典，經由評估證實我們的方法除了比其他方法優異，相對於一般的情緒字典也有更突出的表現。

Nowadays, the advance of social media has led to the explosive growth of opinion data. Therefore, sentiment analysis has attracted a lot of attentions. Currently, sentiment analysis applications are divided into two main approaches, the lexicon-based approach and the machine-learning approach. However, both of them face the challenge of obtaining a large amount of human-labeled training data and corpus. For the lexicon-based approach, it requires a sentiment lexicon (sentiment dictionary) to determine the opinion polarity. There are many existing benchmark sentiment lexicons, but they cannot cover all the domain-specific words meanings. Thus, automatic generation of a domain-specific sentiment lexicon becomes an important task.
In this paper, we propose a framework to automatically generate sentiment lexicon. First, we determine the semantic similarity between two words in the entire unlabeled corpus. We treat the words as nodes and similarities as weighted edges to construct word graphs. A graph-based semi-supervised label propagation method finally assigns the polarity to unlabeled words through the proposed propagation process. Experiments conducted on the microblog data, Twitter, show that our approach leads to a better performance than baseline approaches and general-purpose sentiment dictionaries.

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