Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Abstract

(2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25 (8). pp. 1719 -1731 . ISSN 1041 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/43452/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract-Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. Typically, sentiment classification has been modeled as the problem of training a binary classifier using reviews annotated for positive or negative sentiment. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance because words that occur in the train (source) domain might not appear in the test (target) domain. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised...

Cross-domain Text Classification with Multiple Domains and Disparate Label Sets

“…Bollegala et al, 2013;Crammer et al, 2008;Mansour et al, 2009;Ben-David et al, 2010;Bhatt et al, 2016) and 2) combining pre-trained classifiers (Schweikert and Widmer, 2008;Sun and Shi, 2013;Xu and Sun, 2012;. Our work differentiates in intelligently exploiting selective transferable knowledge from multiple sources unlike existing approaches where multiple sources contribute in a brute-force manner.…”

A Survey Paper on Extraction of Web Services for Effective User Review Sentiment Analysis

“…The sentiment classification methods discussed above can be tuned to work very well on a given domain; however, they may fail in classifying sentiments in a different domain. A cross-domain sentiment classifier using an automatically extracted sentiment thesaurus was proposed by Bollegala et al [11].…”

“…For a wide variety of products and services in different domains, supervised methods are not efficient because it is very expensive to construct labeled data for each product or service. In addition, this model requires a decentsized set of labeled data for model learning on every domain [11].Supervised learning models that require labeled data have been successfully used to build sentiment classifiers for a given domain. In supervised learning model there is problem in domain adaption [10].…”

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“…SFA [5] used some domain-independent features as a bridge to construct domain-special feature clusters. In SST [11] method, it used the related features from source domain to expand vectors in a binary classifier at training and testing times. HeMap [12] tried to use spectral transformation construct common subspace.…”