This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a table as its input data and is applied tothe keyword extraction. The table based algorithms worked successfully in text mining tasks such as text categorization andtext clustering in previous works, and the keyword extraction is able to be mapped into the binary classification where each word isclassified into keyword or non-keyword. In the proposed system, a text which is given as the input is indexed into a list of words, each word is classified by the proposed KNN version, and the words which are classified into keyword are extracted ad the output. The proposed KNN version is empirically validated as the better approach in deciding whether each word is a keyword or non-keyword in news articles and opinions. In using the table based KNN algorithm, it is easier to trace results from categorizing words.