Modeling and Representing Negation in Data-driven Machine Learning-based Sentiment Analysis
نویسنده
چکیده
We 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, we compare 2 methods: the simpler regular expression-based NegEx, and the more sophisticated Conditional Random Field-based LingScope. Additionally, we capture negation implicitly via word biand trigrams. We 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 inand cross-domain, and sentence-level polarity classification. In all subtasks, explicitly modeling negation yields statistically significant better results than not modeling negation or modeling it only implicitly.
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