Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
This addresses a key issue in sentiment analysis for applications using lexicon-based classifiers, though it is incremental as it builds on existing methods for domain adaptation.
The paper tackles the problem of domain-dependent sentiment words that change polarity across domains, proposing a graph-based technique to detect and correct them, with experimental results demonstrating effectiveness on multiple real-world datasets.
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 classification. One major issue with this approach is that many sentiment words 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. 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 real-world datasets.