由於網際網路的快速成長及資訊數量的暴增，如何有效率地抽取能滿足使用者需求的資訊成為一重要的課題。有鑑於某些資訊存在於時效性之特徵，若能分析出其時間性並善加利用，相信對於抽取相關之資訊時能更有效率。常常我們會利用所抽取出足以代表一篇文章的關鍵詞來建立索引，以助於後續之搜尋。在此我們也藉由分析關鍵詞的時間性，進而找出詞彙可能發生的週期進行預測。
　　藉由衡量同一詞彙在不同時間點下的權重，並提出“two stage dichotomy”，亦即利用傅立葉頻譜來將其依發生的頻率分類；之後再將可能有週期性特徵的詞彙依其時間的特徵及經傅立葉轉換後的傅立葉頻譜的特徵找出週期，以進行預測。
　　根據實驗結果顯示，我們的方法不但可行而且成效顯著，利用本篇論文所提出之分析詞彙的方法，的確可以將詞彙依發生的頻繁度進行分類；此外，雖然從分析出來的詞彙的權重無法直接觀察出其週期性，但結合傅立葉頻譜及該詞彙本身時間性的特徵是可以協助找到週期的。

A method to analyze keywords is proposed in this paper. It measures the weights of keywords in different time slots, and classifies the keywords. According to the features of respecting occurrence of keywords and analyzing Fourier spectrum, which transforms the weights of the keyword in different time slot into frequency, the keywords could be classified. Additionally, we propose a new method, combining full and partial periodicity analysis, to predict the occurrence of keywords, which detects the periodicity of one keyword for prediction.
There are few researchers analyzing keywords by time and utilizing Fourier Transform (FT) for analyzing the result to look for the characteristics of the occurrence of the keyword, and followed by analyzing the features of Fourier spectrum transformed from FT to detect the periodicity of the keywords. The experimental results show that our approach has a good performance in analyzing keywords and predicting the periodicity of keywords. Combining full and partial periodicity analysis, we improve the accuracy of prediction.

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