In order to apply any Machine Learning algorithm, we have to convert all non-numerical data to numerical data.

Here I am going to discuss some of the methodolgies to convert text data into numerical vectors. A vector is nothing but a numerical array. Please look into Linear Algebra section to know more about vectors & their properties.

Also by converting text data to numerical array, we can measure similarity between the texts by using Euclidean distance or Cosine similarity

Lets consider the following example.

1. text1: India is a beautiful country

2. text2: I am proud of my country

## BOW model (Bag Of Words)

In this model, each text data is converted to a vector whose length is equal to number of unique words in the whole document. Initially each value in the vector is initilised to zero.

For both text1 & text2, the vector looks as follows initially

I India a am beautiful country is my of proud
0 0 0 0 0 0 0 0 0 0

#### Calculate BOW values

For each of the word in the text_data:
increase the count of the word by 1


(i) text1: (India is a beautiful country)

I India a am beautiful country is my of proud
0 1 1 0 1 1 1 0 0 0

(ii) text2: (I am proud of my country)

I India a am beautiful country is my of proud
1 0 0 1 0 1 0 1 1 1

If we have very large number of unique words, then the size of the vector will be very large

## TF - IDF (Term frequency - Inverse Document Frequency)

#### TF (Term Frequency)

TF is measured for a word in the text data. TF for a word in text data is the probability of that word in the text data.i.e.,

#### IDF (Inverse Document Frequency)

IDF is measured for a word with respect to the whole document. IDF of a word $W_j$ is defined as the log of total number of documents in the corpus to the number of documents that contains the word $W_j$. i.e.,

#### TF-IDF

It is the product of TF & IDF of a word.

So TF-IDF value balances between the rare words in the document & highly frequent words in the corpus. High value of IDF indicates, the word occurs very few times in the whole document corpus. And a high value of TF indicates, the word occurs more number of times in the text document.

1. TF-IDF Vector representation of text1 (India is a beautiful country)

Similary we can calculate TF-IDF values for other words. So TF-IDF vector for text1 can be represented as follows

I India a am beautiful country is my of proud
0 0.06 0.06 0 0.06 0 0.06 0 0 0

There are also many variations of TF-IDF model like TF-IDF word2vec model. I will discuss word2vec in Deep Learning section (…coming soon…)