Automatically determining attitude type and force for sentiment analysis (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Automatically determining attitude type and force for sentiment analysis (Articolo in rivista) (literal)

Anno

• 2009-01-01T00:00:00+01:00 (literal)

Alternative label

• Argamon S.; Bloom K.; Esuli A.; Sebastiani F. (2009)
  Automatically determining attitude type and force for sentiment analysis
  in Lecture notes in computer science
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Argamon S.; Bloom K.; Esuli A.; Sebastiani F. (literal)

Pagina inizio

• 218 (literal)

Pagina fine

• 231 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 5603 (literal)

Rivista

• Lecture notes in computer science (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• In: Human Language Technology. Challenges of the Information Society. pp. 218 - 231. Hans Uszkoreit, Zygmunt Vetulani (eds.). (Lecture Notes in Computer Science, vol. 5603). Heidelberg: Springer Verlag, 2009. (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Linguistic Cognition Laboratory Department of Computer Science, Illinois Institute of Technology, Chicago, CNR-ISTI, Pisa (literal)

Titolo

• Automatically determining attitude type and force for sentiment analysis (literal)

Abstract

• Recent work in sentiment analysis has begun to apply fine-grained semantic distinctions between expressions of attitude as features for textual analysis. Such methods, however, require the construction of large and complex lexicons, giving values for multiple sentiment-related attributes to many different lexical items. For example, a key attribute is what type of attitude is expressed by a lexical item; e.g., beautiful expresses appreciation of an object's quality, while evil expresses a negative judgement of social behavior. In this paper we describe a method for the automatic determination of complex sentiment-related attributes such as attitude type and force, by applying supervised learning to WordNet glosses. Experimental results show that the method achieves good effectiveness, and is therefore well-suited to contexts in which these lexicons need to be generated from scratch. (literal)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• ANDREA ESULI (Unità di personale interno)
• FABRIZIO SEBASTIANI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Automatically determining attitude type and force for sentiment analysis" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• FABRIZIO SEBASTIANI (Unità di personale interno)
• ANDREA ESULI (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Lecture notes in computer science (Rivista)

Insieme di parole chiave di

• Keywords of "Automatically determining attitude type and force for sentiment analysis" (Insieme di parole chiave)