论文标题
在情感词典中检测单词的极性变化
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
论文作者
论文摘要
情感词典有助于情感分析。可以使用情感词典中提供的一组情感词和基于词典的分类器来执行情感分类。这种方法的一个主要问题是，许多情感词是依赖域的。也就是说，它们在某些领域可能是积极的，但在其他一些领域中可能是负面的。我们将这个问题称为单词的域极性变化。检测此类词并纠正其对应用程序域的情感非常重要。在本文中，我们提出了一种基于图的技术来解决此问题。实验结果显示了其对多个现实世界数据集的有效性。
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.
