Abstract
We present a compositional framework for modelling entity-level sentiment (sub)contexts, and demonstrate how holistic multi-entity polarity scoring emerges as a by-product of compositional sentiment parsing. A data set of five annotators’ multi-entity judgements is presented, and a human ceiling is established for the challenging new task. The accuracy of an initial implementation, which includes both supervised learning and heuristic distance-based scoring methods, is 5.6∼6.8 points below the human ceiling amongst sentences and 8.1∼8.7 points amongst phrases.
Keywords
Computer scienceParsingArtificial intelligenceNatural language processingSentiment analysisCeiling (cloud)Task (project management)Set (abstract data type)HeuristicMachine learningInformation retrieval
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Publication Info
- Year
- 2009
- Type
- article
- Pages
- 258-263
- Citations
- 45
- Access
- Closed
External Links
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Cite This
Karo Moilanen,
Stephen Pulman
(2009).
Multi-entity Sentiment Scoring.
, 258-263.