Accessing Positive and Negative Online Opinions

被引：0

作者：

Kang, Hanhoon ^{[1
]}

Yoo, Seong Joon ^{[1
]}

Han, Dongil ^{[1
]}

机构：

[1] Sejong Univ, Sch Comp Engn, Gwangjin Seoul, South Korea

来源：

UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION: APPLICATIONS AND SERVICES, PT III | 2009年 / 5616卷

关键词：

opinion mining; machine learning; text categorization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, an increasing number of people review the comments on each item before they will purchase the commodities and services offered by online shopping malls, Internet blogs, or cafes. However, it is somewhat challenging to routinely read trough all of the comments. The purpose of this study is to introduce some methods to classify the positive or negative review pertaining to the blog comments on a movie written in Korean. For this purpose, a variety of algorithms was used to classify the reviews and allow feature-selection by applying the traditional machine learning method for classifying literature.

引用

页码：359 / 368

页数：10

共 19 条

[1]

[Anonymous], PRACTICAL GUIDE SUPP

[2]

[Anonymous], P 14 INT C MACH LEAR

[3]

[Anonymous], DATA MINING PRACTICA

[4]

Dave K., 2003, holderProceedings Of The 12th International Conference On World Wide Web, P519, DOI [DOI 10.1145/775152.775226, 10.1145/775152.775226]

[5]

Dave K., 2003, P 12 INT WORLD WID W, P512

[6]

DOAN S, 2004, P 4 INT C HYBR INT S

[7]

ISA D, 2008, IEEE T KNOWLEDGE DAT, V20

[8]

JawWon Hwang, 2008, Journal of KISS: Computing Practices, V14, P336

[9]

Joachims T., 1998, MACHINE LEARNING ECM, P137, DOI [10.1007/BFb0026683, DOI 10.1007/BFB0026683]

[10] SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems [J].

Kang, Hanhoon ;

Yoo, Seong Joon .

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2007, E90D (12) :2100-2103

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