1. A method, comprising: obtaining a set of content items; generating a set of clauses from the set of content items; generating a set of n-grams from the set of clauses; using the set of n-grams to automatically extract a set of topics from the set of clauses in the set of content items; for each topic in the set of topics: automatically extracting, by one or more computer systems, a set of attributes that provides a context for the topic from a subset of the content items containing the topic, wherein the set of attributes is extracted as a set of words in a vicinity of the topic contained in each clause in the set of clauses; obtaining a refined set of attributes by removing from the set of attributes one or more high-frequency words that appear in the set of n-grams; using the refined set of attributes to obtain a set of sentiments associated with the topic; and aggregating the set of sentiments into a sentiment distribution for the topic; and displaying, by the one or more computer systems, the refined set of attributes in the context of the topic and the sentiment distribution for the topic to improve understanding of the set of content items by the user without requiring the user to manually analyze the set of content items.
6. The method of claim 1 , wherein automatically extracting the set of topics from the set of clauses in the set of content items comprises: excluding a first n-gram in the set of n-grams from the set of topics when the first n-gram contains a word in the set of stop words in a pre-specified position of the first n-gram; and excluding a second n-gram in the set of n-grams from the set of topics when the second n-gram contains a proportion of words in the set of stop words that exceeds a threshold.