Sentiment Analysis in the News

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 identified three subtasks for news opinion mining: 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. We tested in the experiments described here 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 have a better performance.

BALAHUR Alexandra;  STEINBERGER Ralf;  KABADJOV Mijail;  ZAVARELLA Vanni;  VAN DER GOOT Erik;  HALKIA Stamatia;  POULIQUEN Bruno;  BELYAEVA Jenya; 

2010-06-07

European Language Resources Agency (ELRA)

JRC57132

http://www.lrec-conf.org/proceedings/lrec2010/index.html,    https://publications.jrc.ec.europa.eu/repository/handle/JRC57132,