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001 ocn968303022;

003 OCoLC;

005 20170427122744.0;

006 m o d ;

007 cr unu||||||||;

008 170113s2016 ncua obm 000 0 eng d;

035 a| (Sirsi) o968303022;

035 a| (OCoLC)968303022;

049 a| EREE;

050 0 a| QA76.9.D343;

100 1 a| Huang, Yifan,e| author.=| ^A1319865;

245 12 a| A framework for mining on Twitter data /c| by Yifan Huang.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2016.;

300 a| 64 pages :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 1.63 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2016.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: Qin Ding.;

500 a| Title from PDF t.p. (viewed February 6, 2017).;

520 3 a| Motivated by the increasing need of information retrieval from social media, a lexicon-based approach Tweet Sentiment Classifier (TSC) is presented to determine sentiment from tweet along with a systematic software for twitter data statistics analysis and topic extraction. The TSC uses annotated dictionaries of words (SentiWordNet) and has a negation detector. While the LDA topic model uses Gibbs Sampling. The entire system is unsupervised. Without the need of training, it has significant advantage on speed comparing to supervised methods. It is robust to provide consistent satisfying results from different topics of twitter data. The performance of the TSC also outperforms one of the baseline sentiment analysis methods.;

504 a| Includes bibliographical references.;

650 0 a| Data mining.=| ^A408868;

650 0 a| Information retrievalx| Computer programs.=| ^A835449;

650 0 a| Social media.=| ^A770054;

653 a| Text mining;

653 a| Sentiment Analysis;

700 1 a| Ding, Qin,e| degree supervisor.=| ^A1464811;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/6026;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4604971;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604971-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/13/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604971-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/13/2017x| ETDz| HERESOURCE;

A framework for mining on Twitter data / by Yifan Huang.

Author/creator	Huang, Yifan author.
Other author	Ding, Qin, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2016.
Description	64 pages : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Data mining. Information retrieval--Computer programs. Social media.

Summary	Motivated by the increasing need of information retrieval from social media, a lexicon-based approach Tweet Sentiment Classifier (TSC) is presented to determine sentiment from tweet along with a systematic software for twitter data statistics analysis and topic extraction. The TSC uses annotated dictionaries of words (SentiWordNet) and has a negation detector. While the LDA topic model uses Gibbs Sampling. The entire system is unsupervised. Without the need of training, it has significant advantage on speed comparing to supervised methods. It is robust to provide consistent satisfying results from different topics of twitter data. The performance of the TSC also outperforms one of the baseline sentiment analysis methods.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: Qin Ding.
General note	Title from PDF t.p. (viewed February 6, 2017).
Dissertation note	M.S. East Carolina University 2016.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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A framework for mining on Twitter data / by Yifan Huang.

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