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A Visualizable Evidence-Driven Approach for Authorship Attribution
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In: https://dl.acm.org/citation.cfm?doid=2744298.2699910 (2015)
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Ten Years of Rich Internet Applications: A Systematic Mapping Study, and Beyond
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On the localness of software
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In: http://macbeth.cs.ucdavis.edu/cache-model.pdf (2014)
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Approximate Semantic Matching of Events for the Internet of Things
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Q.: Scale based region growing for scene text detection
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In: http://www.stat.ucla.edu/%7Ejunhua.mao/papers/Scale_based_region_growing_ACM_MM13.pdf (2013)
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Gigatensor: scaling tensor analysis up by 100 times - algorithms and discoveries
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In: http://www.cs.cmu.edu/~christos/PUBLICATIONS/kdd12-gigatensor.pdf (2012)
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Distributional Semantics with Eyes: Using Image Analysis to Improve Computational Representations of Word Meaning
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In: http://clic.cimec.unitn.it/marco/publications/bruni-etal-acmmm-2012.pdf (2012)
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Integrating document clustering and . . .
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In: http://users.cis.fiu.edu/~taoli/pub/a14-wang.pdf (2011)
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Folks in folksonomies: social link prediction from shared metadata
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In: http://hal.archives-ouvertes.fr/docs/00/42/98/86/PDF/wsdm141-schifanella.pdf (2010)
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Semantic lexicon adaptation for use in query interpretation
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In: http://www.ra.ethz.ch/cdstore/www2010/www/p1167.pdf (2010)
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Classifying latent user attributes in twitter
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In: https://csc-869-mlog.googlecode.com/files/p37-rao.pdf (2010)
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Spatiotemporal mapping of Wikipedia concepts
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In: http://comupedia.org/adrian/articles/jcdl75-popescu.pdf (2010)
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An Information-extraction system for Urdu—a resource-poor language
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In: http://www.cedar.buffalo.edu/~rohini/Papers/ACM-TALIP.pdf (2010)
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Transliteration for resource-scarce languages
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In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010)
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MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis
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In: http://clef.isti.cnr.it/2009/working_notes/morpho-papers/bernhard-paperCLEF2009.pdf (2009)
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OpinionMiner: a novel machine learning system for web opinion mining and extraction
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In: http://www.cedar.buffalo.edu/~rohini/Papers/KDD_Jin.pdf (2009)
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Abstract:
Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision. The OpinionMiner system designed in this work aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. Opinion expressions are identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that employed rule-based or statistical techniques, we propose a novel machine learning approach built under the framework of lexicalized HMMs. The approach naturally integrates multiple important linguistic features into automatic learning. In this paper, we describe the architecture and main components of the system. The evaluation of the proposed method is presented based on processing the online product reviews from Amazon and other publicly available datasets.
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Keyword:
Algorithms; Design; Experimentation; Human Factors Keywords Opinion Mining; Lexicalized HMMs; Sentiment Analysis
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URL: http://www.cedar.buffalo.edu/~rohini/Papers/KDD_Jin.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.7054
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Word sense disambiguation: a survey
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In: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf (2009)
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Sentiment analysis of blogs by combining lexical knowledge with text classification
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In: http://www.prem-melville.com/publications/pooling-multinomials-kdd09.pdf (2009)
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A 2-poisson model for probabilistic coreference of named entities for improved text retrieval
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In: http://www.comp.nus.edu.sg/~nght/pubs/sigir09.pdf (2009)
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