Cross-domain sentiment classification using grams derived from syntax trees and an adapted naive Bayes approach

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dc.contributor.author Cheeti, Srilaxmi
dc.date.accessioned 2012-04-27T19:51:16Z
dc.date.available 2012-04-27T19:51:16Z
dc.date.issued 2012-04-27
dc.identifier.uri http://hdl.handle.net/2097/13733
dc.description.abstract There is an increasing amount of user-generated information in online documents, includ- ing user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as either positive, negative or neutral based on the words present in the document. Supervised learning approaches have been successfully used for sentiment classification in domains that are rich in labeled data. Some of these approaches make use of features such as unigrams, bigrams, sentiment words, adjective words, syntax trees (or variations of trees obtained using pruning strategies), etc. However, for some domains the amount of labeled data can be relatively small and we cannot train an accurate classifier using the supervised learning approach. Therefore, it is useful to study domain adaptation techniques that can transfer knowledge from a source domain that has labeled data to a target domain that has little or no labeled data, but a large amount of unlabeled data. We address this problem in the context of product reviews, specifically reviews of movies, DVDs and kitchen appliances. Our approach uses an Adapted Naive Bayes classifier (ANB) on top of the Expectation Maximization (EM) algorithm to predict the sentiment of a sentence. We use grams derived from complete syntax trees or from syntax subtrees as features, when training the ANB classifier. More precisely, we extract grams from syntax trees correspond- ing to sentences in either the source or target domains. To be able to transfer knowledge from source to target, we identify generalized features (grams) using the frequently co-occurring entropy (FCE) method, and represent the source instances using these generalized features. The target instances are represented with all grams occurring in the target, or with a reduced grams set obtained by removing infrequent grams. We experiment with different types of grams in a supervised framework in order to identify the most predictive types of gram, and further use those grams in the domain adaptation framework. Experimental results on several cross-domains task show that domain adaptation approaches that combine source and target data (small amount of labeled and some unlabeled data) can help learn classifiers for the target that are better than those learned from the labeled target data alone. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Adapted naive bayes algorithm en_US
dc.subject Cross domain sentiment classification en_US
dc.subject Grams en_US
dc.subject Domain adaptation en_US
dc.subject Syntax subtrees en_US
dc.title Cross-domain sentiment classification using grams derived from syntax trees and an adapted naive Bayes approach en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor Doina Caragea en_US
dc.subject.umi Computer Engineering (0464) en_US
dc.subject.umi Computer Science (0984) en_US
dc.subject.umi Information Science (0723) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth May en_US

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