Authors:
Srilaxmi Cheeti
;
Ana Stanescu
and
Doina Caragea
Affiliation:
Kansas State University, United States
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.