This paper introduces an aspect based model for analysing search queries, where queries are represented as aspects or concepts instead of “bag of words” or strings. A search query may consist of multiple aspects, while some aspects are covered well in the search results and some others are underrepresented. We think the underrepresented aspects are the main reason for the retrieval of irrelevant documents. This paper introduces novel algorithms that identify query aspects and identify underrepresented aspects. This model has many applications and this paper focuses on three of them: query difficulty prediction, query expansion and interactive query expansion. The main idea is that a hard query is a query with multiple aspects and some of the aspects are underrepresented, and a query can be improved or expanded by adding terms that are semantically related to the underrepresented aspects. Our experiments show that our aspect based methods significantly outperform existing methods.