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

César Alfaro (), Javier Cano-Montero (), Javier Gómez (), Javier Moguerza () and Felipe Ortega ()

Annals of Operations Research, 2016, vol. 236, issue 1, 197-213

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. Copyright Springer Science+Business Media New York 2016

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
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10479-013-1449-6

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