machine learning mode are suitable for using word features as x, and y as click rate of text Hi I want to extract words of english texts using click rate with machine learning model. Now I know the click rate of text, and I know How to extract words(unigram) of each text, for example, there are about 10000 texts,and the click rate of each text is provided. How to extract words features for click rate. How to extract key words and compute the importance of each word for computing click rate.
You can build a dictionary of character sequences (words) and for each instance of text you will count the occurrence of these words. You can either use groupings of characters n-grams or words them selves using bag-of-words.

n-grams

n-grams is a feature extraction technique for language based data. It segments the Strings such that roots of words can be found, ignoring verb endings, pluralities etc...

The segmentation works as follows: 

The String: Hello World

2-gram: "He", "el", "ll", "lo", "o ", " W", "Wo", "or", "rl", "ld"
3-gram: "Hel", "ell", "llo", "lo ", "o W", " Wo", "Wor", "orl", "rld"
4-gram: "Hell", "ello", "llo ", "lo W", "o Wo", " Wor", "Worl", "orld"

Thus in your example, if we use 4-grams, truncations of the word Hello would appear to be the same. And this similarity would be captured by your features.

Bag-of-words

Bag-of-Words builds a dictionary of the words it has seen during the training phase. Then using the word the frequency of each word in the example a vector is created. This can then be used with any standard machine learning technique.