TY - GEN
T1 - Languages' impact on emotional classification methods
AU - Eilertsen, Alexander C.
AU - Rose, Dennis Hojbjerg
AU - Erichsen, Peter Langballe
AU - Christensen, Rasmus Engesgaard
AU - Nath, Rudra Pratap Deb
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Cross-Language Analysis
KW - Emotional Classification
KW - Natural Language Processing
KW - Sentiment Analysis
KW - Text-to-Emotion Analysis
UR - http://www.scopus.com/inward/record.url?scp=85074147423&partnerID=8YFLogxK
U2 - 10.15439/2019F143
DO - 10.15439/2019F143
M3 - Article in proceeding
T3 - Federated Conference on Computer Science and Information Systems
SP - 277
EP - 286
BT - Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019
A2 - Ganzha, Maria
A2 - Maciaszek, Leszek
A2 - Maciaszek, Leszek
A2 - Paprzycki, Marcin
PB - IEEE
T2 - 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019
Y2 - 1 September 2019 through 4 September 2019
ER -