@inproceedings{zhou-etal-2021-hot,
    title = "Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons",
    author = "Zhou, Jie  and
      Wu, Yuanbin  and
      Sun, Changzhi  and
      He, Liang",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.49/",
    doi = "10.18653/v1/2021.eacl-main.49",
    pages = "608--618",
    abstract = "Modelling a word{'}s polarity in different contexts is a key task in sentiment analysis. Previous works mainly focus on domain dependencies, and assume words' sentiments are invariant within a specific domain. In this paper, we relax this assumption by binding a word{'}s sentiment to its collocation words instead of domain labels. This finer view of sentiment contexts is particularly useful for identifying commonsense sentiments expressed in neural words such as ``big'' and ``long''. Given a target (e.g., an aspect), we propose an effective ``perturb-and-see'' method to extract sentiment words modifying it from large-scale datasets. The reliability of the obtained target-aware sentiment lexicons is extensively evaluated both manually and automatically. We also show that a simple application of the lexicon is able to achieve highly competitive performances on the unsupervised opinion relation extraction task."
}