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According to the term (word) frequency of documents the TF-IDF (Term Frequency- Inverse Term Frequency) estimates the importance of word. In the below code segment, the goal of first Map Reduce job is to count the occurrences of each word in each document. After getting the word frequency, the reducer need to sum the total number of words for each document. In Second MapReduce, the term frequency is calculated and it is passed to third Map Reduce class. The third Map Reduce computes inverse term frequency then it combines both values to calculate score for each word.
//First MapReduce
//calculate Term Frequency
//count the number of words in each of the documents in the corpus.
k1-documentId
v1-line of the document
Map Phase input:<k1, v1>
//Extract word from line using StringTokenizer class
k2-word and documetID
v2-1 (word count in MAP should be 1)
output(k2, v2)
Reduce Phase input<k2, List<v2>>
Frequency of word=Sum of the v2 for each key “word and documetID”
k3-word and documetID
v3-Frequency of word
output(k3, v3)
//Second MapReduce
//calculate Term Frequency
//calculate total words for each document
Map Phase input:<k3, v3>
k4-documentID
v4-Frequency of word
output(k4, v4)
Reduce Phase input<k4, List<v4>>
Totalwords for all documents (N)=Sum the total words for all documents (v4)
TermFrequency of word =Frequency of word / N
k5-Word
v5-TermFrequency
output(k5, v5)
//Third MapReduce
//calculate TFIDF
Map Phase input:<k5, v5>
output(k5, v5)
K5-word
v5-TermFrequency of word
Reduce Phase input<k5, v5>
N=Total number of documents in corpus
n=number of documents in corpus where the word appears.
TFIDF=v5 * log(N/n)
k6-Word
v6-TFIDF
output(k6, v6)
Input
Document1 file
TermFrequency
TFIDF
