東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Review mining from online media.

Liu, Yang.

Linked to FindBook

Google Book

Amazon

博客來

Review mining from online media.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Review mining from online media./
Author:	Liu, Yang.
Description:	123 p.
Notes:	Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5616.
Contained By:	Dissertation Abstracts International70-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR51735
ISBN:	9780494517352

Review mining from online media.
Liu, Yang.

Review mining from online media. - 123 p.

Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5616.

Thesis (Ph.D.)--York University (Canada), 2009.

Online reviews are becoming an ever popular source of information. In this thesis, we study three important problems in the contexts of automatic review mining from online media, and propose a set of new techniques to address the challenges arising therein.

ISBN: 9780494517352Subjects--Topical Terms:

626642
Computer Science.

Review mining from online media.
LDR:01958nam 2200253 4500 001 1392727
005 20110218131340.5
008 130515s2009 ||||||||||||||||| ||eng d
020 $a 9780494517352
035 $a (UMI)AAINR51735
035 $a AAINR51735
040 $a UMI $c UMI
100 1 $a Liu, Yang. $3 1026508
245 1 0 $a Review mining from online media.
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5616.
502 $a Thesis (Ph.D.)--York University (Canada), 2009.
520 $a Online reviews are becoming an ever popular source of information. In this thesis, we study three important problems in the contexts of automatic review mining from online media, and propose a set of new techniques to address the challenges arising therein.
520 $a Mining opinions and sentiments from reviews presents unique challenges that cannot be easily addressed by conventional text mining methods. Therefore, we first propose novel approaches that can provide a comprehensive understanding of the sentiments reflected in the reviews. Equipped with such approaches, we then develop models and algorithms that can use the extracted opinions and sentiments for predicting product sales performance. As a case study, we investigate how to predict box office revenues from Weblogs, which have recently received a lot of attention due to its high popularity. Orthogonal to the problem of identifying reviewer opinions, we consider how to automatically evaluate the helpfulness of reviews, and consequently develop novel methods to identify the most helpful reviews for a particular product. Properly used, we expect such models and algorithms to be highly helpful in various aspects of business intelligence.
590 $a School code: 0267.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a York University (Canada). $3 1017889
773 0 $t Dissertation Abstracts International $g 70-09B.
790 $a 0267
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR51735