For the Affective Text task at SemEval-2007, University Paris 7's system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and a subset of WordNetAffect). We use a parser to find the head word, considering that it has a major importance. We also detect contrasts (between positive and negative words) that shift valence. Our knowledge-based system achieves high accuracy on emotion and valence annotation. These results show that working with linguistic techniques and a broad-coverage lexicon is a viable approach to sentiment analysis of headlines.
An affective text task on news headlines at SemEval 2007 for emotion and valence level identification has been described in REF .
UPAR7: A knowledge-based system for headline sentiment tagging
Fourth International Workshop on Semantic Evaluations (SemEval-2007)
Computer Science