Implementation Details of the Text Classification Project

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Implementation Details of the Text Classification Project Prerak Sanghvi Computer Science and Engineering Department State University of New York at Buffalo Spring 2001

Feature Selection Step We select keywords from text by using some way of scoring words. Here, Information Gain is being used. For each unique word, the number of documents in each class, in which the word occurs, is noted.

Feature Selection Step - Algorithm for each document d in training set for each word w if w has been encountered before increment the document count for Category(d) in record for w else create a new data record for w using the record for w, calculate Information Gain Select NUM_KEYWORDS with highest Information Gain.

Feature Selection Word Cat1 Cat2 Cat3 Cat4 …. Cat20 Nation 5 15 4 3 1 God 12 13 7 9 Soccer 6 2 19 News 10

Information Gain G (t) = - i=1 to m Pr (ci) log Pr (ci) + Pr(t) i=1 to m Pr (ci|t) log Pr (ci|t) + Pr(t) i=1 to m Pr (ci|t) log Pr (ci|t) Pr (ci) = 1/ 20 Pr (t) = (i=1 to m Catm(t)) / (i=1 to m j=1 to w Catm(j)) Pr (ci|t) = Catm (t) / i=1 to m Catm(t)

Classification Algorithm KeyWord Cat1 Cat2 Cat3 Cat4 …. Cat20 Nation 5 15 4 3 1 God 12 13 7 9 Soccer 6 2 19 News 10