@inproceedings{schulder-etal-2018-automatically,
    title = "Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for {G}erman",
    author = "Schulder, Marc  and
      Wiegand, Michael  and
      Ruppenhofer, Josef",
    editor = "Bender, Emily M.  and
      Derczynski, Leon  and
      Isabelle, Pierre",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-1213/",
    pages = "2516--2528",
    abstract = "In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb {\textquotedblleft}abandon{\textquotedblright} in {\textquotedblleft}abandon all hope{\textquotedblright}. This is similar to how negation words like {\textquotedblleft}not{\textquotedblright} can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available."
}