Artikel

Automatic generation of lexica for sentiment polarity shifters

Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.

Automatic generation of lexica for sentiment polarity shifters

Urheber*in: Schulder, Marc; Wiegand, Michael; Ruppenhofer, Josef

Namensnennung 4.0 International

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Sprache
Englisch

Thema
Negativer Polaritätsausdruck
Polarität
Lexikalische Semantik
Klassifikation
Maschinelles Lernen
Lexikon
Sprache

Ereignis
Geistige Schöpfung
(wer)
Schulder, Marc
Wiegand, Michael
Ruppenhofer, Josef
Ereignis
Veröffentlichung
(wer)
Cambridge : Cambridge University Press
(wann)
2020-07-31

URN
urn:nbn:de:bsz:mh39-99895
Letzte Aktualisierung
06.03.2025, 09:00 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Schulder, Marc
  • Wiegand, Michael
  • Ruppenhofer, Josef
  • Cambridge : Cambridge University Press

Entstanden

  • 2020-07-31

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