working with text data in we talked about two kinds of features that can represent properties of the datacontinuous features that describe quantityand categorical features that are items from fixed list there is third kind of feature that can be found in many applicationswhich is text for exampleif we want to classify an email message as either legitimate email or spamthe content of the email will certainly contain important information for this classification task or maybe we want to learn about the opinion of politician on the topic of immigration herethat individual' speeches or tweets might provide useful information in customer servicewe often want to find out if message is complaint or an inquiry we can use the subject line and content of message to automatically determine the customer' intentwhich allows us to send the message to the appropriate departmentor even send fully automatic reply text data is usually represented as stringsmade up of characters in any of the examples just giventhe length of the text data will vary this feature is clearly very different from the numeric features that we've discussed so farand we will need to process the data before we can apply our machine learning algorithms to it types of data represented as strings before we dive into the processing steps that go into representing text data for machine learningwe want to briefly discuss different kinds of text data that you might encounter text is usually just string in your datasetbut not all string features should be treated as text string feature can sometimes represent categorical variablesas we discussed in there is no way to know how to treat string feature before looking at the data