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Titel: Automatic generation of lexica for sentiment polarity shifters
VerfasserIn: Schulder, Marc
Wiegand, Michael
Ruppenhofer, Josef
Sprache: Englisch
Titel: Natural language engineering
Bandnummer: 27
Heft: 2
Verlag/Plattform: Cambridge University Press
Erscheinungsjahr: 2020
Freie Schlagwörter: Sentiment analysis
Sentiment polarity
Lexical semantics
Lexicon generation
Negation content words
DDC-Sachgruppe: 400 Sprache, Linguistik
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: 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.
DOI der Erstveröffentlichung: 10.1017/S135132492000039X
URL der Erstveröffentlichung: https://www.cambridge.org/core/journals/natural-language-engineering/article/automatic-generation-of-lexica-for-sentiment-polarity-shifters/E6028995177428DAFB724669F74CDF44
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-444101
hdl:20.500.11880/39679
http://dx.doi.org/10.22028/D291-44410
ISSN: 1469-8110
1351-3249
Datum des Eintrags: 17-Feb-2025
Fakultät: P - Philosophische Fakultät
Fachrichtung: P - Sprachwissenschaft und Sprachtechnologie
Professur: P - Prof. Dr. Dietrich Klakow
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons