Abstract
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.
Original language | English |
---|---|
Title of host publication | PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation |
Pages | 297-306 |
Number of pages | 10 |
Volume | 1 |
Publication status | Published - 1 Dec 2009 |
Event | 23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23 - Hong Kong, Hong Kong Duration: 3 Dec 2009 → 5 Dec 2009 |
Conference
Conference | 23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23 |
---|---|
Country/Territory | Hong Kong |
City | Hong Kong |
Period | 3/12/09 → 5/12/09 |
Keywords
- Linear classifier
- Opinion mining
- Sentiment classification
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science (miscellaneous)