Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons
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.
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