東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Social Network Opinion and Posts Min...

Mumu, Tamanna.

Linked to FindBook

Google Book

Amazon

博客來

Social Network Opinion and Posts Mining for Community Preference Discovery.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Social Network Opinion and Posts Mining for Community Preference Discovery./
Author:	Mumu, Tamanna.
Description:	114 p.
Notes:	Source: Masters Abstracts International, Volume: 52-01.
Contained By:	Masters Abstracts International52-01(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR87124
ISBN:	9780494871249

Social Network Opinion and Posts Mining for Community Preference Discovery.
Mumu, Tamanna.

Social Network Opinion and Posts Mining for Community Preference Discovery. - 114 p.

Source: Masters Abstracts International, Volume: 52-01.

Thesis (M.Sc.)--University of Windsor (Canada), 2013.

The popularity of posts, topics, and opinions on social media websites and the influence ability of users can be discovered by analyzing the responses of users (e.g., likes/dislikes, comments, ratings). Existing web opinion mining systems such as OpinionMiner is based on opinion text similarity scoring of users' review texts and product ratings to generate database table of features, functions and opinions mined through classification to identify arriving opinions as positive or negative on user-service networks or interest networks (e.g., Amazon.com). These systems are not directly applicable to user-user networks or friendship networks (e.g., Facebook.com) since they do not consider multiple posts on multiple products, users' relationships (such as influence), and diverse posts and comments.

ISBN: 9780494871249Subjects--Topical Terms:

626642
Computer Science.

Social Network Opinion and Posts Mining for Community Preference Discovery.
LDR:02494nam a2200313 4500 001 1958554
005 20140421080414.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9780494871249
035 $a (MiAaPQ)AAIMR87124
035 $a AAIMR87124
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mumu, Tamanna. $3 2093676
245 1 0 $a Social Network Opinion and Posts Mining for Community Preference Discovery.
300 $a 114 p.
500 $a Source: Masters Abstracts International, Volume: 52-01.
500 $a Advisers: Christie I. Ezeife; Ziad Kobti.
502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2013.
520 $a The popularity of posts, topics, and opinions on social media websites and the influence ability of users can be discovered by analyzing the responses of users (e.g., likes/dislikes, comments, ratings). Existing web opinion mining systems such as OpinionMiner is based on opinion text similarity scoring of users' review texts and product ratings to generate database table of features, functions and opinions mined through classification to identify arriving opinions as positive or negative on user-service networks or interest networks (e.g., Amazon.com). These systems are not directly applicable to user-user networks or friendship networks (e.g., Facebook.com) since they do not consider multiple posts on multiple products, users' relationships (such as influence), and diverse posts and comments.
520 $a In this thesis, we propose a new influence network (IN) generation algorithm (Opinion Based IN:OBIN) through opinion mining of friendship networks (like Facebook.com). OBIN mines opinions using extended OpinionMiner that considers multiple posts and relationships (influences) between users. Approach used includes frequent pattern mining algorithm for determining community (positive or negative) preferences for a given product as input to standard influence maximization algorithms like CELF for target marketing. Experiments and evaluations show the effectiveness of OBIN over CELF in large-scale friendship networks.
520 $a KEYWORDS Influence Analysis, Recommendation, Ranking, Sentiment Classification, Large Scale Network, Social Network, Opinion Mining, Text Mining.
590 $a School code: 0115.
650 4 $a Computer Science. $3 626642
650 4 $a Web Studies. $3 1026830
650 4 $a Information Science. $3 1017528
690 $a 0984
690 $a 0646
690 $a 0723
710 2 $a University of Windsor (Canada). $b COMPUTER SCIENCE. $3 2093677
773 0 $t Masters Abstracts International $g 52-01(E).
790 $a 0115
791 $a M.Sc.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR87124