A multi-stage method for content classification and opinion mining on weblog comments

被引：38

作者：

Alfaro, Cesar ^{[1
]}

Cano-Montero, Javier ^{[1
]}

Gomez, Javier ^{[1
]}

Moguerza, Javier M. ^{[1
]}

Ortega, Felipe ^{[1
]}

机构：

[1] Rey Juan Carlos Univ, Dept Stat & Operat Res, Camino El Molino S-N, Fuenlabrada, Spain

来源：

ANNALS OF OPERATIONS RESEARCH | 2016年 / 236卷 / 01期

关键词：

Multi-stage method; Text classification; Text analytics; Opinion mining; Sentiment analysis; k-Nearest neighbors; Support vector machines;

D O I：

10.1007/s10479-013-1449-6

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, we illustrate how to combine supervised machine learning algorithms and unsupervised learning techniques for sentiment analysis and opinion mining purposes. To this end, we describe a multi-stage method for the automatic detection of different opinion trends. The proposal has been tested on real textual data available from comments introduced in a weblog, connected to organizational and administrative affairs in a public educational institution. The use of the described tool, given its potential impact to obtain valuable knowledge from opinion streams created by commenters, may be straightforwardly extended, for example, to the detection of opinion trends concerning policy decision making or electoral campaigns.

引用

页码：197 / 213

页数：17

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