Languages' impact on emotional classification methods

Alexander C. Eilertsen, Dennis Hojbjerg Rose, Peter Langballe Erichsen, Rasmus Engesgaard Christensen, Rudra Pratap Deb Nath

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)
18 Downloads (Pure)

Abstrakt

There is currently a lack of research concerning whether Emotional Classification (EC) research on a language is applicable to other languages. If this is the case then we can greatly reduce the amount of research needed for different languages. Therefore, we propose a framework to answer the following null hypothesis: The change in classification accuracy for Emotional Classification caused by changing a single preprocessor or classifier is independent of the target language within a significance level of p = 0.05. We test this hypothesis using an English and a Danish data set, and the classification algorithms: Support-Vector Machine, Naive Bayes, and Random Forest. From our statistical test, we got a p-value of 0.12852 and could therefore not reject our hypothesis. Thus, our hypothesis could still be true. More research is therefore needed within the field of cross-language EC in order to benefit EC for different languages.
OriginalsprogEngelsk
TitelProceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019
RedaktørerMaria Ganzha, Leszek Maciaszek, Leszek Maciaszek, Marcin Paprzycki
Antal sider10
ForlagIEEE
Publikationsdatosep. 2019
Sider277-286
Artikelnummer8860017
ISBN (Elektronisk)9788395541605
DOI
StatusUdgivet - sep. 2019
Begivenhed2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019 - Leipzig, Tyskland
Varighed: 1 sep. 20194 sep. 2019

Konference

Konference2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019
LandTyskland
ByLeipzig
Periode01/09/201904/09/2019
SponsorIntel
NavnFederated Conference on Computer Science and Information Systems
Vol/bind18
ISSN2300-5963

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