文本為人類歷史足跡的載體，從朝代歷史至個人日記，記錄著當代人類的文化思想、風俗民情與科技發展，隨著時代推演這些紀錄不再侷限於牛皮紙張或土瓦竹簡等實體載具，以更多元的數位型式記載在網路虛擬世界。而文本往往必須委由專家才能解讀出其中心思想，隨著文字分析技術的興起，愈來愈多學者研發藉由量化技術找出文字蘊含的意義，以因應資訊氾濫時代中快速篩選資訊，提供專家以外另一種角度的解讀。
主題式分析是文字分析的重要研究議題，透過界定關鍵詞與區隔文本屬性使得文本解析更為精確及有效率，本文以常用的TF-IDF (term frequency inverse document frequency)與處理語意的常見工具詞網(WordNet)為基礎，提出核心詞彙與篩選標籤特徵應用，探討因文章長短所造成的不穩定性與特殊領域詞彙關係問題(Magnini and Cavaglia, 2000)。本文利用《臺灣社會科學引文索引》(TSSCI)、美國專利、《人民日報》等三個文本作為分析對象，建構該文本的語意關係與相關之應用。分析發現TSSCI與美國專利的文本的分類準確率近八成，但若文本篇數過少時會因為雜訊太強無法呈現語意關係；而文本標籤(Label)間若是風格寫作上的差異，本文提出的主題分類無法歸類出較準確的分類結果，這可能也是《人民日報》文本分類準確率不佳的原因，但仍能透過該標籤的特徵(Feature)了解該時期的特殊主題。

Text is the carrier of the human history. From the official history to the personal diary, it records the culture, thoughts, customs, and technological developments of human beings. With the progress of computer technology, text recordings are no longer restricted to physical vehicles, such as kraft paper or earthen bamboo slips, and they can be recorded in various digital forms. With the rise of interest in quantifying text analysis, more and more scholars are dedicated in the technologic development of text analysis and apply them to explore the text meaning. Many people think that computer technology, such as machine learning and artificial intelligence, can help us relax the burden of human experts in seeking the meaning under the text.
Topic analysis is an important research topic in text analysis. It makes text parsing faster by defining keywords and separating text attributes. This paper proposes the application of core vocabulary and screening tag features based on the commonly used TF-IDF (term frequency inverse document frequency) and the common tool word network (WordNet). We will apply them in exploring the relationship between instability caused by the length of the article and vocabulary (Magnini and Cavaglia, 2000). We use the Taiwan Social Science Citation Index (TSSCI), the U.S. patent, and the People's Daily as the study materials. The results of text analysis show that the classification accuracies of TSSCI and U.S. patent texts are nearly 80%. However, if the number of article is too small, then the noise will distort the analysis and semantic relations. Also, we found the style writing would influence the accuracy of topic classification, which may be the reason why the People’s Daily text classification accuracy is not good.

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