Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F143

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 277–286 (2019)

Full text

Abstract. 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.

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