UPAR7: a knowledge-based system for headline sentiment tagging

  • Authors:
  • François-Régis Chaumartin

  • Affiliations:
  • Lattice/Talana -- Université Paris, Paris, France

  • Venue:
  • SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
  • Year:
  • 2007

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Abstract

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 WordNet-Affect). 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.