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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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| 2 |
Ten Years of Rich Internet Applications: A Systematic Mapping Study, and Beyond
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| 3 |
On the localness of software
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In: http://macbeth.cs.ucdavis.edu/cache-model.pdf (2014)
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| 4 |
Approximate Semantic Matching of Events for the Internet of Things
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| 5 |
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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| 6 |
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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| 7 |
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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| 8 |
Integrating document clustering and . . .
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In: http://users.cis.fiu.edu/~taoli/pub/a14-wang.pdf (2011)
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| 9 |
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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| 10 |
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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| 12 |
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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| 13 |
Spatiotemporal mapping of Wikipedia concepts
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In: http://comupedia.org/adrian/articles/jcdl75-popescu.pdf (2010)
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| 14 |
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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| 15 |
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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| 16 |
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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| 17 |
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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| 18 |
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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| 19 |
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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Abstract:
The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of such analysis is to characterize the sentiment expressed in blogs about specific brands and products. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Most previous work in this area uses prior lexical knowledge in terms of the sentiment-polarity of words. In contrast, some recent approaches treat the task as a text classification problem, where they learn to classify sentiment based only on labeled training data. In this paper, we present a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples. Empirical results on diverse domains show that our approach performs better than using background knowledge or training data in isolation, as well as alternative approaches to using lexical knowledge with text classification.
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Keyword:
Categories and Subject Descriptors I.2.6 [Artificial Intelligence; Economics; Experimentation; I.5.1 [Pattern Recognition; Learning; Models General Terms Algorithms
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URL: http://www.prem-melville.com/publications/pooling-multinomials-kdd09.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.1842
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| 20 |
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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