Latent Semantic Analysis – Part 1

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7 Responses to “Latent Semantic Analysis – Part 1”

  1. ganesh Says:

    awesome dude——-
    ur work is very much helpful for me…

  2. sudheer Says:

    can u plz make clear ur assumption and declaration of row -column and vice versa , are different..

    countMatrix [length of ArrayCollection][ total number of documents]

    Also i have posted a question to ur mail did u check.. reply me..

  3. shakthydoss Says:

    CountMatrix [ No.of.row ] [ No.of.column]

    No.of.row is nothing but no of words in all documents
    No.of.column is nothing but no of documents in corpus

  4. sudheer Says:

    One more question i have to you is:

    Is that Number of words in all documents (in previous post ) include:

    1) terms remained after Stopword removal stemming (index words)?? or full document(s) tokens.

    2)the above index words resulted , are they assumed duplicated in index file u have?? or removed repeated words

    Waiting 4 reply

    • shakthydoss Says:

      It is terms remaining after stop-words removing and stemming process.

      Index will not have duplications.

  5. TV TUAN Says:

    You use tdidf (not tfifd), so it might work with word similarity, since you can somehow define the tdidf of the query Q for each document by couting occurences of all words in Q in that document ? (T,F ?), however, if you want to solve doc-similarity then tdidf will not fix the case. I think we need to nomalize both term frequency, not only doc frequency, how do we define the tfidf for document query Q?

  6. huangzy Says:

    I hava a question:

    In this article, you wrote the words followed:
    For example, the word “market” (whose annotated value is 2 in row) appears 4 times in a particular document (whose annotated value is 5) in column.

    4 should not be in the 2nd row, 5th column in the count matrix (the 4 in blue color is 5th row, 2nd column in your picture)? Or am I wrong?

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