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

César Alfaro (), Javier Cano-Montero (), Javier Gómez (), Javier M. Moguerza () and Felipe Ortega ()
Additional contact information
César Alfaro: Rey Juan Carlos University
Javier Cano-Montero: Rey Juan Carlos University
Javier Gómez: Rey Juan Carlos University
Javier M. Moguerza: Rey Juan Carlos University
Felipe Ortega: Rey Juan Carlos University

Annals of Operations Research, 2016, vol. 236, issue 1, No 10, 197-213

Abstract: Abstract 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.

Keywords: Multi-stage method; Text classification; Text analytics; Opinion mining; Sentiment analysis; k-Nearest neighbors; Support vector machines (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-013-1449-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:236:y:2016:i:1:d:10.1007_s10479-013-1449-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-013-1449-6

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().