原文链接：http://www.cnblogs.com/youth0826/archive/2012/08/11/2633688.html [MATLAB] Simple TF-IDF implementation

Term-Frequency word weighting scheme is one of most used in normalization of document-term matrices in text mining and information retrieval.

See wikipedia for details.

 

 

function Y = tfidf( X )

% FUNCTION computes TF-IDF weighted word histograms.

%

%   Y = tfidf( X );

%

% INPUT :

%   X        - document-term matrix (documents in columns)

%

% OUTPUT :

%   Y        - TF-IDF weighted document-term matrix

%

% get term frequencies

X = tf(X);

% get inverse document frequencies

I = idf(X);

% apply weights for each document

for j=1:size(X, 2)

    X(:, j) = X(:, j)*I(j);

end

Y = X;

function X = tf(X)

% SUBFUNCTION computes word frequencies

% for every word

for i=1:size(X, 1)

    % get word i counts for all documents

    x = X(i, :);

    % sum all word i occurences in the whole collection

    sumX = sum( x );

    % compute frequency of the word i in the whole collection

    if sumX ~= 0

        X(i, :) = x / sum(x);

    else

        % avoiding NaNs : set zero to never appearing words

        X(i, :) = 0;

    end

end

function I = idf(X)

% SUBFUNCTION computes inverse document frequencies

% m - number of terms or words

% n - number of documents

[m, n]=size(X);

% allocate space for document idf's

I = zeros(n, 1);

% for every document

for j=1:n

    % count non-zero frequency words

    nz = nnz( X(:, j) );

    % if not zero, assign a weight:

    if nz

        I(j) = log( m / nz );

    end

end

posted on 2012-08-11 15:30 Shicai Yang 阅读(...) 评论(...) 编辑 收藏

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