國立虎尾科技大學 |

Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media./
作者:	Ding, Wanying.
面頁冊數:	1 online resource (199 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355590746

Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media.
Ding, Wanying.

Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media. - 1 online resource (199 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: A.

Thesis (Ph.D.)--Drexel University, 2017.

Includes bibliographical references

The desire, want, and thinking of the majority of people on one issue or problem is called Public Opinion. Public opinions have various impacts on many perspectives of human society. Thus public opinion analysis has long been an important research topic. Old-styled public opinion analysis is more about linguistics and limited by opinion resources and analysis tools. Social Media has enriched the opinion resources but also brought new problems, such as Big Data Problem, Information Fragmentation Problem, Time Sensitive Evolution Problem, and Visualization Problem.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355590746Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media.
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245 1 0 $a Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media.
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300 $a 1 online resource (199 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: A.
500 $a Advisers: Xiaohua Hu; Chaomei Chen.
502 $a Thesis (Ph.D.)--Drexel University, 2017.
504 $a Includes bibliographical references
520 $a The desire, want, and thinking of the majority of people on one issue or problem is called Public Opinion. Public opinions have various impacts on many perspectives of human society. Thus public opinion analysis has long been an important research topic. Old-styled public opinion analysis is more about linguistics and limited by opinion resources and analysis tools. Social Media has enriched the opinion resources but also brought new problems, such as Big Data Problem, Information Fragmentation Problem, Time Sensitive Evolution Problem, and Visualization Problem.
520 $a With the development of Information Science and Computer Scinece, many algorithms can help in more efficient opinion analysis. This thesis focuses on using data mining methods to facilitate online public opinion analysis on aspect level, namely Aspect Level Public Opinion Detection,Tracking and Visualization.
520 $a With respect to public opinion detection, traditional machine learning methods require laborious training data labeling and careful feature engineer. This thesis focuses on Statistical Learning and Deep Learning. Statistical Learning frees people from labeling data. We have proposed three statistical methods: Hybrid HDP-LDA Model, Similarity Dependency Dirichlet Process, and Semi-Supervised Dirichlet Process. Experiment results have confirmed their ability in unsupervised or semi-supervised opinion detection. Deep Learning frees people from feature engineering. We have proposed a Hierarchical Attention Network for Opinion Summarization. Experiment results have shown its higher accuaracy in document opinion summarization.
520 $a In public opinion tracking part, different from traditional methods, which need manually discretize timeline into discrete episodes, we focus on Stochastic Process and Online Deep Learning methods. Hawkes Process and Online LSTM-AutoEncoder are two models we have proposed. We have tested them on online time-sensitive datasets and proved their ability for opinion tracking.
520 $a This thesis has also created several visualizations to present results from opinion detection and tracking. In order to visualize opinion detection results, Stacked Bar Chart, Divergent Bar Chart, Hierarchical Edge Bundle and Treemap have been utilized. In order to visualize opinion tracking results, Line Chart, Linked Histogram Graph, Alluvial Flow and Unaligned Alluvial Flow are deployed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Web studies. $3 1148502
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a Drexel University. $b Information Studies. $3 1184437
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10684072 $z click for full text (PQDT)