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Knowledge transformation for cross-domain sentiment classification
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In: http://users.cis.fiu.edu/~taoli/pub/sigir09-p716-li.pdf (2009)
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Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
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In: http://users.cis.fiu.edu/%7Etaoli/pub/sigir08-p307-wang.pdf (2008)
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Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence, Chi-square Statistic, and a Hybrid Method
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In: http://mall.psy.ohio-state.edu/LexicalSemantics/DingLiPeng06.pdf
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| 4 |
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
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In: http://users.cs.fiu.edu/~taoli/tenure/fp557-Wang.pdf
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| 5 |
Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations
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In: http://www.siam.org/proceedings/datamining/2010/dm10_026_lit.pdf
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Abstract:
With the explosion of user-generated web2.0 content in the form of blogs, wikis and discussion forums, the Internet has rapidly become a massive dynamic repository of public opinion on an unbounded range of topics. A key enabler of opinion extraction and summarization is sentiment classification: the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a topic of interest. Building high-quality sentiment classifiers using standard text categorization methods is challenging due to the lack of labeled data in a target domain. In this paper, we consider the problem of cross-domain sentiment analysis: can one, for instance, download rated movie reviews from rottentomatoes.com or IMBD discussion forums, learn linguistic expressions and sentiment-laden terms that generally characterize opinionated commentary and then successfully transfer this knowledge to the target domain, thereby building high-quality sentiment models without manual effort? We outline a novel sentiment transfer mechanism based on constrained non-negative matrix tri-factorizations of termdocument matrices in the source and target domains. The constrained matrix factorization framework naturally incorporates document labels via a least squares penalty incurred by a certain linear model and enables direct and explicit knowledge transfer across different domains. We obtain promising empirical results with this approach.
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Keyword:
Nonnegative matrix; Sentiment analysis; Transfer learning
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.2968
http://www.siam.org/proceedings/datamining/2010/dm10_026_lit.pdf
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