Modeling and Representing Negation in Data-driven Machine Learning-based Sentiment Analysis
The authors propose a scheme for explicitly modeling and representing negation of word n-grams in an augmented word n-gram feature space. For the purpose of negation scope detection, they compare 2 methods: the simpler regular expression-based NegEx, and the more sophisticated conditional random field-based LingScope. Additionally, they capture negation implicitly via word bi- and trigrams. They analyze the impact of explicit and implicit negation modeling as well as their combination on several data-driven machine learning-based sentiment analysis subtasks, i.e. document-level polarity classification, both in- and cross-domain, and sentence-level polarity classification.