Online news editors ask themselves the same question many times: what is missing in this news article to go online?This is not an easy question to be answered by computational linguistic methods.In this work, we address this important question and characterise the constituents of news article editorial quality.More specifically, we identify 14 aspects related to the content of news articles.Through a correlation analysis, we quantify their independence and relation to assessing an article's editorial quality.We also demonstrate that the identified aspects, when combined together, can be used effectively in quality control methods for online news.

Reputation analysis is naturally linked to a sentiment analysis task of the targeted entities. This analysis leverages on a sentiment lexicon that includes general sentiment words and domain specific jargon. However, in most cases target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer an entity reputation. Sometimes, the entity became a reference in the domain and is vastly cited as an example of a highly reputable entity. For example, in the movies domain it is not uncommon to see reviews citing Batman or Anthony Hopkins as esteemed references. In this paper we describe an unsupervised method for performing a simultaneous-analysis of the reputation of multiple named-entities. Our method jointly extracts named entities reputation and a domain specific sentiment lexicon. The objective is two-fold: (1) named-entities are naturally ranked by our method and (2) we can build a reputation graph of the domain's named entities. This framework has immediate applications in terms of visualization or search by reputation.