TF-IDF词项权重计算

一、TF-IDF

词项频率:

df:term frequency。 term在文档中出现的频率.tf越大,词项越重要.

文档频率:

tf:document frequecy。有多少文档包括此term，df越大词项越不重要.

词项权重计算公式：

tf-idf=tf(t,d)*log(N/df(t))

• W(t,d):the weight of the term in document d
• tf(t,d):the frequency of term t in document d
• N:the number of documents
• df(t):the number of documents that contain term t

二、JAVA实现

package com.javacore.algorithm;

import java.util.Arrays;
import java.util.List;

/**
 * Created by bee on 17/3/13.
 * @version 1.0
 * @author blog.csdn.net/napoay
 */
public class TfIdfCal {



    /**
     *calculate the word frequency
     * @param doc word vector of a doc
     * @param term  a word
     * @return the word frequency of a doc
     */
    public double tf(List<String> doc, String term) {

        double termFrequency = 0;
        for (String str : doc) {
            if (str.equalsIgnoreCase(term)) {
                termFrequency++;
            }
        }
        return termFrequency / doc.size();
    }


    /**
     *calculate the document frequency
     * @param docs the set of all docs
     * @param term a word
     * @return the number of docs which contain the word
     */

    public int df(List<List<String>> docs, String term) {
        int n = 0;
        if (term != null && term != "") {

            for (List<String> doc : docs) {
                for (String word : doc) {
                    if (term.equalsIgnoreCase(word)) {
                        n++;
                        break;
                    }
                }
            }
        } else {
            System.out.println("term不能为null或者空串");
        }

        return n;
    }


    /**
     *calculate the inverse document frequency
     * @param docs  the set of all docs
     * @param term  a word
     * @return  idf
     */

    public double idf(List<List<String>> docs, String term) {

        System.out.println("N:"+docs.size());
        System.out.println("DF:"+df(docs,term));
        return  Math.log(docs.size()/(double)df(docs,term));
    }


    /**
     * calculate tf-idf
     * @param doc a doc
     * @param docs document set
     * @param term a word
     * @return inverse document frequency
     */
    public double tfIdf(List<String> doc, List<List<String>> docs, String term) {

        return tf(doc, term) * idf(docs, term);
    }


    public static void main(String[] args) {

        List<String> doc1 = Arrays.asList("人工", "智能", "成为", "互联网", "大会", "焦点");
        List<String> doc2 = Arrays.asList("谷歌", "推出", "开源", "人工", "智能", "系统", "工具");
        List<String> doc3 = Arrays.asList("互联网", "的", "未来", "在", "人工", "智能");
        List<String> doc4 = Arrays.asList("谷歌", "开源", "机器", "学习", "工具");
        List<List<String>> documents = Arrays.asList(doc1, doc2, doc3,doc4);


        TfIdfCal calculator = new TfIdfCal();

        System.out.println(calculator.tf(doc2, "开源"));
        System.out.println(calculator.df(documents, "开源"));
        double tfidf = calculator.tfIdf(doc2, documents, "谷歌");
        System.out.println("TF-IDF (谷歌) = " + tfidf);
        System.out.println(Math.log(4/2)*1.0/7);

    }


}

执行结果:

0.14285714285714285
2
N:4
DF:2
TF-IDF (谷歌) = 0.09902102579427789