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10.1371/journal.pone.0122855 http://scihub22266oqcxt.onion/10.1371/journal.pone.0122855 C4395415!4395415!25874768     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2015 ; 10 (4): ä Nephropedia Template TP {{tp|p=25874768|t=2015. Using Support Vector Machine Ensembles for Target Audience Classification on Twitter |pdf=|usr=}}{{25874768}} gab.com Text Using Support Vector Machine Ensembles for Target Audience Classification on Twitter JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=25874768 #FOAvip The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. Twit Text FOAVip Using Support Vector Machine Ensembles for Target Audience Classification on Twitter ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=25874768 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25874768 #FOAvip English Wikipedia <ref>{{Cite pmid|25874768}}</ref> Using Support Vector Machine Ensembles for Target Audience Classification on Twitter #MMPMID25874768 Lo SL; Chiong R; Cornforth D PLoS One 2015[]; 10 (4): ä PMID25874768show ga The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. äUsing Support Vector Machine Ensembles for Target Audience Classificat(epmc).pdf DeepDyve Pubget Overpricing