Sentiment classification considering negation and contrast transition

Negation and contrast transition are two kinds of linguistic phenomena which are popularly used to reverse the sentiment polarity of some words and sentences. In this paper, we propose an approach to incorporate their classification information into our sentiment classification system: First, we classify sentences into sentiment reversed and non-reversed parts. Then, represent them as two different bags-of-words. Third, present three general strategies to do classification with two-bag-of-words modeling. We collect a large-scale product reviews involving five domains and conduct our experiments on them. The experimental results show that incorporating both negation and contrast transition information is effective and performs robustly better than traditional machine learning approach (based on one-bag-of-words modeling) across five different domains.