Machine Learning :: Text feature extraction (tf-idf) – Part I

Short introduction to Vector Space Model (VSM)

In information retrieval or text mining, the term frequency – inverse document frequency (also called tf-idf), is a well know method to evaluate how important is a word in a document. tf-idf are is a very interesting way to convert the textual representation of information into a Vector Space Model (VSM), or into sparse features, we’ll discuss more about it later, but first, let’s try to understand what is tf-idf and the VSM.

VSM has a very confusing past, see for example the paper The most influential paper Gerard Salton Never Wrote that explains the history behind the ghost cited paper which in fact never existed; in sum, VSM is an algebraic model representing textual information as a vector, the components of this vector could represent the importance of a term (tf–idf) or even the absence or presence (Bag of Words) of it in a document; it is important to note that the classical VSM proposed by Salton incorporates local and global parameters/information (in a sense that it uses both the isolated term being analyzed as well the entire collection of documents). VSM, interpreted in a lato sensu, is a space where text is represented as a vector of numbers instead of its original string textual representation; the VSM represents the features extracted from the document.

Let’s try to mathematically define the VSM and tf-idf together with concrete examples, for the concrete examples I’ll be using Python (as well the amazing scikits.learn Python module).

Going to the vector space

The first step in modeling the document into a vector space is to create a dictionary of terms present in documents. To do that, you can simple select all terms from the document and convert it to a dimension in the vector space, but we know that there are some kind of words (stop words) that are present in almost all documents, and what we’re doing is extracting important features from documents, features do identify them among other similar documents, so using terms like “the, is, at, on”, etc.. isn’t going to help us, so in the information extraction, we’ll just ignore them.

Let’s take the documents below to define our (stupid) document space:

Train Document Set:

d1: The sky is blue.
d2: The sun is bright.

Test Document Set:

d3: The sun in the sky is bright.
d4: We can see the shining sun, the bright sun.

Now, what we have to do is to create a index vocabulary (dictionary) of the words of the train document set, using the documents d1 and d2 from the document set, we’ll have the following index vocabulary denoted as \mathrm{E}(t) where the t is the term:

   \mathrm{E}(t) =   \begin{cases}   1, & \mbox{if } t\mbox{ is ``blue''} \\   2, & \mbox{if } t\mbox{ is ``sun''} \\   3, & \mbox{if } t\mbox{ is ``bright''} \\   4, & \mbox{if } t\mbox{ is ``sky''} \\   \end{cases}


Note that the terms like “is” and “the” were ignored as cited before. Now that we have an index vocabulary, we can convert the test document set into a vector space where each term of the vector is indexed as our index vocabulary, so the first term of the vector represents the “blue” term of our vocabulary, the second represents “sun” and so on. Now, we’re going to use the term-frequency to represent each term in our vector space; the term-frequency is nothing more than a measure of how many times the terms present in our vocabulary \mathrm{E}(t) are present in the documents d3 or d4, we define the term-frequency as a couting function:

   \mathrm{tf}(t,d) = \sum\limits_{x\in d} \mathrm{fr}(x, t)

where the \mathrm{fr}(x, t) is a simple function defined as:

   \mathrm{fr}(x,t) =   \begin{cases}   1, & \mbox{if } x = t \\   0, & \mbox{otherwise} \\   \end{cases}

So, what the tf(t,d) returns is how many times is the term t is present in the document d. An example of this, could be  tf(``sun'', d4) = 2 since we have only two occurrences of the term “sun” in the document d4. Now you understood how the term-frequency works, we can go on into the creation of the document vector, which is represented by:

   \displaystyle \vec{v_{d_n}} =(\mathrm{tf}(t_1,d_n), \mathrm{tf}(t_2,d_n), \mathrm{tf}(t_3,d_n), \ldots, \mathrm{tf}(t_n,d_n))

Each dimension of the document vector is represented by the term of the vocabulary, for example, the \mathrm{tf}(t_1,d_2) represents the frequency-term of the term 1 or t_1 (which is our “blue” term of the vocabulary) in the document d_2.

Let’s now show a concrete example of how the documents d_3 and d_4 are represented as vectors:

   \vec{v_{d_3}} = (\mathrm{tf}(t_1,d_3), \mathrm{tf}(t_2,d_3), \mathrm{tf}(t_3,d_3), \ldots, \mathrm{tf}(t_n,d_3)) \\   \vec{v_{d_4}} = (\mathrm{tf}(t_1,d_4), \mathrm{tf}(t_2,d_4), \mathrm{tf}(t_3,d_4), \ldots, \mathrm{tf}(t_n,d_4))

which evaluates to:

   \vec{v_{d_3}} = (0, 1, 1, 1) \\   \vec{v_{d_4}} = (0, 2, 1, 0)

As you can see, since the documents d_3 and d_4 are:

d3: The sun in the sky is bright.
d4: We can see the shining sun, the bright sun.

The resulting vector \vec{v_{d_3}} shows that we have, in order, 0 occurrences of the term “blue”, 1 occurrence of the term “sun”, and so on. In the \vec{v_{d_3}}, we have 0 occurences of the term “blue”, 2 occurrences of the term “sun”, etc.

But wait, since we have a collection of documents, now represented by vectors, we can represent them as a matrix with |D| \times F shape, where |D| is the cardinality of the document space, or how many documents we have and the F is the number of features, in our case represented by the vocabulary size. An example of the matrix representation of the vectors described above is:

   M_{|D| \times F} =   \begin{bmatrix}   0 & 1 & 1 & 1\\   0 & 2 & 1 & 0   \end{bmatrix}

As you may have noted, these matrices representing the term frequencies tend to be very sparse (with majority of terms zeroed), and that’s why you’ll see a common representation of these matrix as sparse matrices.

Python practice

Environment Used: Python v.2.7.2, Numpy 1.6.1, Scipy v.0.9.0, Sklearn (Scikits.learn) v.0.9.

Since we know the  theory behind the term frequency and the vector space conversion, let’s show how easy is to do that using the amazing scikit.learn Python module.

Scikit.learn comes with lots of examples as well real-life interesting datasets you can use and also some helper functions to download 18k newsgroups posts for instance.

Since we already defined our small train/test dataset before, let’s use them to define the dataset in a way that scikit.learn can use:

train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.",
    "We can see the shining sun, the bright sun.")

In scikit.learn, what we have presented as the term-frequency, is called CountVectorizer, so we need to import it and create a news instance:

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()

The CountVectorizer already uses as default “analyzer” called WordNGramAnalyzer, which is responsible to convert the text to lowercase, accents removal, token extraction, filter stop words, etc… you can see more information by printing the class information:

print vectorizer

analyzer__stop_words=set(['all', 'six', 'less', 'being', 'indeed', 'over', 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', (...)

Let’s create now the vocabulary index:

print vectorizer.vocabulary
{'blue': 0, 'sun': 1, 'bright': 2, 'sky': 3}

See that the vocabulary created is the same as E(t) (except because it is zero-indexed).

Let’s use the same vectorizer now to create the sparse matrix of our test_set documents:

smatrix = vectorizer.transform(test_set)

print smatrix

(0, 1)        1
(0, 2)        1
(0, 3)        1
(1, 1)        2
(1, 2)        1

Note that the sparse matrix created called smatrix is a Scipy sparse matrix with elements stored in a Coordinate format. But you can convert it into a dense format:


matrix([[0, 1, 1, 1],
........[0, 2, 1, 0]], dtype=int64)

Note that the sparse matrix created is the same matrix M_{|D| \times F} we cited earlier in this post, which represents the two document vectors \vec{v_{d_3}} and \vec{v_{d_4}}.

We’ll see in the next post how we define the idf (inverse document frequency) instead of the simple term-frequency, as well how logarithmic scale is used to adjust the measurement of term frequencies according to its importance, and how we can use it to classify documents using some of the well-know machine learning approaches.

I hope you liked this post, and if you really liked, leave a comment so I’ll able to know if there are enough people interested in these series of posts in Machine Learning topics.

As promised, here is the second part of this tutorial series.


The classic Vector Space Model

The most influential paper Gerard Salton never wrote

Wikipedia: tf-idf

Wikipedia: Vector space model

Scikits.learn Examples


21 Sep 11 fixed some typos and the vector notation
22 Sep 11 – fixed import of sklearn according to the new 0.9 release and added the environment section
02 Oct 11 – fixed Latex math typos
18 Oct 11 – added link to the second part of the tutorial series
04 Mar 11 – Fixed formatting issues

59 thoughts on “Machine Learning :: Text feature extraction (tf-idf) – Part I

  1. Thanks, the mix of actual examples with theory is very handy to see the theory in action and helps retain the theory better. Though in this particular post, i was a little disappointed as i felt it ended too soon. I would like more longer articles. But i guess longer articles turn off majority of the readers.

  2. Very interesting blogpost, I’m sure up for more on the topic :)!

    I recently had to handle VSM & TF-IDF in Python too, in a text-processing task of returning most similar strings of an input-string. I haven’t looked at scikits.learn, but it sure looks useful and straightforward.

    I use Gensim (VSM for human beings: together with NLTK for preparing the data (aka word tokenizing, lowering words, and removing stopwords). I can highly recommend both libraries!

    For some more (slightly out of date) details of my approach, see:

    Thanks for the post, and looking forward to part II :).

  3. Thanks for this, most interesting. I look forward to reading your future posts on the subject.

  4. Thanks a lot for this writeup. At times its really good to know what is cooking backstage behind all fancy and magical functions.

  5. Thank you! It is very useful for me to learn about the vector space model. But I am having some doubts, please make me clear.
    1. In my work I have added terms,Inverse document frequency. I want to achieve more accuracy. So I want to add some more, please suggest me…

  6. Great post, I will certainly try this out.

    I would be interested to see a similar detailed break down on using something like svmlight in conjunction with these techniques.


  7. Hey again – my outputs are slighlty different to yours.. Think there may have been changes to the module on scikit-learn.

    Will let you know what i find out.

    Take care

  8. Hello there,
    so basically the class feature_selection.text.Vectorizer in Sklearn is now deprecated and replaced by feature_selection.text.TfidfVectorizer.

    The whole module has been completely re-factored –
    here’s the change-log from the Scikit-learn website:

    See under ‘API changes summary’ for what’s changed

    Just thought I’d give you a headsup about this.
    Enjoyed your post regardless!
    Take care

  9. Thank you for your post. I am currently working on a way how to index documents, but with vocabulary terms taken from a thesaurus in SKOS format.
    Your posts are interesting and very helpful to me.

  10. Hey thanks for the very insightful post! I had no idea modules existed in Python that could do that for you ( I calculated it the hard way :/)

    Just curious did you happen to know about using tf-idf weighting as a feature selection or text categorization method. I’ve been looking at many papers (most from China for some reason) but am finding numerous ways of approaching this question.

    If there’s any advice or direction to steer me towards as far as additional resources, that would be greatly appreciated.

  11. Hi
    I am using python-2.7.3, numpy-1.6.2-win32-superpack-python2.7, scipy-0.11.0rc1-win32-superpack-python2.7, scikit-learn-0.11.win32-py2.7
    I tried to repeat your steps but I couldn´t print the vectorizer.vocabulary (see below).
    Any suggestions?
    Andres Soto
    >>> train_set = (“The sky is blue.”, “The sun is bright.”)
    >>> test_set = (“The sun in the sky is bright.”,
    “We can see the shining sun, the bright sun.”)
    >>> from sklearn.feature_extraction.text import CountVectorizer
    >>> vectorizer = CountVectorizer()
    >>> print vectorizer
    CountVectorizer(analyzer=word, binary=False, charset=utf-8,
    charset_error=strict, dtype=, input=content,
    lowercase=True, max_df=1.0, max_features=None, max_n=1, min_n=1,
    preprocessor=None, stop_words=None, strip_accents=None,
    token_pattern=bww+b, tokenizer=None, vocabulary=None)
    >>> vectorizer.fit_transform(train_set)
    <2×6 sparse matrix of type '’
    with 8 stored elements in COOrdinate format>
    >>> print vectorizer.vocabulary

    Traceback (most recent call last):
    File “”, line 1, in
    print vectorizer.vocabulary
    AttributeError: ‘CountVectorizer’ object has no attribute ‘vocabulary’

  12. I tried to fix the parameters of CountVectorizer (analyzer = WordNGramAnalyzer, vocabulary = dict) but it didn’t work. Therefore I decided to install sklearn 0.9 and it works, so we could say that everything is OK but I still would like to know what is wrong with version sklearn 0.11

    1. Hello Andres, what I know is that this API has changed a lot on the sklearn 0.10/0.11, I heard some discussions about these changes but I can’t remember where right now.

  13. Thanks for the great overview, looks like the part 2 link is broken. It would be great if you could fix it. Thank You.

  14. I am using a mac and running 0.11 version but I got the following error I wonder how i change this according to the latest api

    >> train_set
    (‘The sky is blue.’, ‘The sun is bright.’)
    >>> vectorizer.fit_transform(train_set)
    <2×6 sparse matrix of type '’
    with 8 stored elements in COOrdinate format>
    >>> print vectorizer.vocabulary
    Traceback (most recent call last):
    File “”, line 1, in
    AttributeError: ‘CountVectorizer’ object has no attribute ‘vocabulary’
    >>> vocabulary

  15. Hello, Mr. Perone! Thank you very much, I’m newbie in TF-IDF and your posts have helped me a lot to understand it. Greetings from Japan^^

  16. very well written. Good example presented in a form that makes it easy to follow and understand. Curious now to read more…
    Thank you for sharing your knowledge
    Thomas, Germany

  17. Hi. I am having trouble understanding how to compute tf-idf weights for a text file I have which contains 300k lines of text. Each line is considered as a document. Example excerpt from the text file:

    hacking hard
    jeetu smart editor
    shyamal vizualizr
    setting demo hacks
    vivek mans land
    social routing guys minute discussions learn photography properly
    naseer ahmed yahealer
    sridhar vibhash yahoo search mashup
    vaibhav chintan facebook friend folio
    vaibhav chintan facebook friend folio
    judgess comments

    I’m pretty confused as to what I should do. Thanks

  18. Thanks
    A well written clear explanation

    Definitely a reference when taking the first steps in text mining.

  19. thank you very much. this post helped me alot. very well written and clear explanation.

  20. Awesome stuff. I really appreciate the simplicity and clarity of the information. A great, great help.

  21. Thanks for the great post. You have explained it in simple words, so that a novice like me can understand. Will move on to read the next part!

  22. Solution to question of Andres and Gavin:

    >>> print vectorizer.vocabulary_

    (with underscore at the end in new versions of scikit!)
    will output:

    {u’blue’: 0, u’bright’: 1, u’sun’: 4, u’is’: 2, u’sky’: 3, u’the’: 5}

    1. Hey brother,

      Do you know exactly what is the difference between (vectorizer.vocabulary_) and (vectorizer.get_feature_names() )?

  23. Very nice post!
    You made tf-idf look really interesting. I am looking forward to some more such posts.

  24. This is Great!
    Really helpful for starters like me!
    Thanks & Keep up the Good Work!


  25. Could you tell me please what is the difference between feature names and vocabulary_ ?

    I printed them both after vectorizing,, they seem having different words??

    Also, I need to print out the most informative words in each class, could you suggest me a way please?


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