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Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

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SKU: PROJ1413 Categories: 2013 Projects, 2014 Projects, Datamining Projects, Final Year Projects, Online Java Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java project list, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Abstract— Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus. 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, < Final Year Projects > 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 data set containing Amazon user reviews for different types of products. We conduct an extensive empirical analysis of the proposed method on single- and multisource domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus. Moreover, our comparisons against the SentiWordNet, a lexical resource for word polarity, show that the created sentiment-sensitive thesaurus accurately captures words that express similar sentiments.

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