Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees

Srilaxmi Cheeti, Ana Stanescu, Doina Caragea

2013

Abstract

Online product reviews contain information that can assist in the decision making process of new customers looking for various products. To assist customers, supervised learning algorithms can be used to categorize the reviews as either positive or negative, if large amounts of labeled data are available. However, some domains have few or no labeled instances (i.e., reviews), yet a large number of unlabeled instances. Therefore, domain adaptation algorithms that can leverage the knowledge from a source domain to label reviews from a target domain are needed. We address the problem of classifying product reviews using domain adaptation algorithms, in particular, an Adapted Naïve Bayes classifier, and features derived from syntax trees. Our experiments on several cross-domain product review datasets show that this approach produces accurate domain adaptation classifiers for the sentiment classification task.

Paper Citation

in Harvard Style

Cheeti S., Stanescu A. and Caragea D. (2013). Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013) ISBN 978-989-8565-75-4, pages 169-176. DOI: 10.5220/0004546501690176

in Bibtex Style

@conference{kdir13,
author={Srilaxmi Cheeti and Ana Stanescu and Doina Caragea},
title={Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)},
year={2013},
pages={169-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004546501690176},
isbn={978-989-8565-75-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)
TI - Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees
SN - 978-989-8565-75-4
AU - Cheeti S.
AU - Stanescu A.
AU - Caragea D.
PY - 2013
SP - 169
EP - 176
DO - 10.5220/0004546501690176