Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features
| Autor: | Michael Wiegand, Josef Ruppenhofer, Marc Schulder |
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| Rok vydání: | 2016 |
| Předmět: |
Training set
business.industry Computer science 02 engineering and technology computer.software_genre Linguistics Annotation Categorization 020204 information systems Noun 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Word (computer architecture) |
| Zdroj: | HLT-NAACL |
| DOI: | 10.18653/v1/n16-1092 |
| Popis: | We examine different features and classifiers for the categorization of opinion words into actor and speaker view. To our knowledge, this is the first comprehensive work to address sentiment views on the word level taking into consideration opinion verbs, nouns and adjectives. We consider many high-level features requiring only few labeled training data. A detailed feature analysis produces linguistic insights into the nature of sentiment views. We also examine how far global constraints between different opinion words help to increase classification performance. Finally, we show that our (prior) word-level annotation correlates with contextual sentiment views. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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