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Creators/Authors contains: "Shuai Wang, Guangyi Lv"

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  1. (, Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021)
    Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment analysis. One major issue with this approach is that many sentiment words (from the lexicon) are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words from a sentiment lexicon. Detecting such words and correcting their sentiment for an application domain is very important. In this paper, we propose a graph-based technique to tackle this problem. Experimental results show its effectiveness on multiple datasets from different domains. 
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