Keyword Selection from Textual Data using Table based K Nearest Neighbor

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 workedsuccessfully in text mining tasks such as text categorization andtext clustering in previous works, and the keyword extraction isable to be mapped into the binary classification where each word isclassified into keyword or non-keyword. In the proposed system, atext which is given as the input is indexed into a list of words,each word is classified by the proposed KNN version, and the wordswhich are classified into keyword are extracted ad the output. Theproposed KNN version is empirically validated as the better approachin deciding whether each word is a keyword or non-keyword in newsarticles and opinions. In using the table based KNN algorithm, it iseasier to trace results from categorizing words.