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doi:10.22028/D291-44410
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 |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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automatic-generation-of-lexica-for-sentiment-polarity-shifters.pdf | 864,77 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons