Text classification aims to assign a text instance into one or more class(es) in a predefined set of classes. This collection of experiments demonstrates the steps of the Text Classification Template on how to build and deploy a text classification model.
The goal of text classification is to assign some piece of text to one or more predefined classes or categories. The piece of text could be a document, news article, search query, email, tweet, support tickets, customer feedback, user product review etc. Applications of text classification include categorizing newspaper articles and news wire contents into topics, organizing web pages into hierarchical categories, filtering spam email, sentiment analysis, predicting user intent from search queries, routing support tickets, and analyzing customer feedback. As part of the _Azure Machine Learning_ offering, _Microsoft_ provides a template to help data scientists easily build and deploy a text classification solution. In this document, you will learn how to use and customize the template through a demo use case. The following graphic presents the workflow of the template. Each step in the workflow corresponds to an Azure ML experiment. The experiments must run in order because the output of one experiment is the input to the next. ![image-overall-pipeline] [image-overall-pipeline]:https://az712634.vo.msecnd.net/samplesimg/v1/T2/overall-pipeline.png