  Google users have different intents from their queries such as acquiring
information, buying products, comparing or simulating services, looking for
products, and so on. Understanding the right intention of users helps to
provide i) better content on web pages from the Search Engine Optimization
(SEO) perspective and ii) more user-satisfying results from the search engine
perspective. In this study, we aim to identify the user query's intent by
taking advantage of Google results and machine learning methods. Our proposed
approach is a clustering model that exploits some features to detect query's
intent. A list of keywords extracted from the clustered queries is used to
identify the intent of a new given query. Comparing the clustering results with
the intents predicted by filtered keywords show the efficiency of the extracted
keywords for detecting intents.
