  Recent years have brought a significant growth in the volume of research in
sentiment analysis, mostly on highly subjective text types (movie or product
reviews). The main difference these texts have with news articles is that their
target is clearly defined and unique across the text. Following different
annotation efforts and the analysis of the issues encountered, we realised that
news opinion mining is different from that of other text types. We identified
three subtasks that need to be addressed: definition of the target; separation
of the good and bad news content from the good and bad sentiment expressed on
the target; and analysis of clearly marked opinion that is expressed
explicitly, not needing interpretation or the use of world knowledge.
Furthermore, we distinguish three different possible views on newspaper
articles - author, reader and text, which have to be addressed differently at
the time of analysing sentiment. Given these definitions, we present work on
mining opinions about entities in English language news, in which (a) we test
the relative suitability of various sentiment dictionaries and (b) we attempt
to separate positive or negative opinion from good or bad news. In the
experiments described here, we tested whether or not subject domain-defining
vocabulary should be ignored. Results showed that this idea is more appropriate
in the context of news opinion mining and that the approaches taking this into
consideration produce a better performance.
