TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Schulder, Marc
A1  - Wiegand, Michael
A1  - Ruppenhofer, Josef
T1  - Automatic generation of lexica for sentiment polarity shifters
JF  - Natural Language Engineering
N2  - 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.
KW  - Negativer Polaritätsausdruck
KW  - Polarität
KW  - Lexikalische Semantik
KW  - Klassifikation
KW  - Maschinelles Lernen
KW  - sentiment analysis
KW  - sentiment polarity
KW  - lexical semantics
KW  - lexicon generation
KW  - negation content words
KW  - Lexikon
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-99895
SN  - 1469-8110
SS  - 1469-8110
U6  - https://doi.org/10.1017/S135132492000039X
DO  - https://doi.org/10.1017/S135132492000039X
VL  - 27
IS  - 2
SP  - 153
EP  - 179
PB  - Cambridge University Press
CY  - Cambridge
ER  -