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Authors: Srilaxmi Cheeti ; Ana Stanescu and Doina Caragea

Affiliation: Kansas State University, United States

ISBN: 978-989-8565-75-4

Keyword(s): Domain Adaptation, Sentiment Classification, Adapted Na¨ıve Bayes, Syntax Trees.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
Cheeti, S.; 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

@conference{kdir13,
author={Srilaxmi Cheeti. and 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},
}

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 - Cheeti, S.
AU - Stanescu, A.
AU - Caragea, D.
PY - 2013
SP - 169
EP - 176
DO - 10.5220/0004546501690176

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