Export Publication

The publication can be exported in the following formats: APA (American Psychological Association) reference format, IEEE (Institute of Electrical and Electronics Engineers) reference format, BibTeX and RIS.

Export Reference (APA)
De Souza, G., Lopes, M., Fernandes, A., Leithardt, V. & Crocker, P. (2023). Comparison between sentiment analysis approaches applied to digital games. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI). Aveiro: IEEE.
Export Reference (IEEE)
D. S. Adão et al.,  "Comparison between sentiment analysis approaches applied to digital games", in 2023 18th Iberian Conf. on Information Systems and Technologies (CISTI), Aveiro, IEEE, 2023
Export BibTeX
@inproceedings{adão2023_1764921091641,
	author = "De Souza, G. and Lopes, M. and Fernandes, A. and Leithardt, V. and Crocker, P.",
	title = "Comparison between sentiment analysis approaches applied to digital games",
	booktitle = "2023 18th Iberian Conference on Information Systems and Technologies (CISTI)",
	year = "2023",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.23919/cisti58278.2023.10211536",
	publisher = "IEEE",
	address = "Aveiro",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/10211536/authors#authors"
}
Export RIS
TY  - CPAPER
TI  - Comparison between sentiment analysis approaches applied to digital games
T2  - 2023 18th Iberian Conference on Information Systems and Technologies (CISTI)
AU  - De Souza, G.
AU  - Lopes, M.
AU  - Fernandes, A.
AU  - Leithardt, V.
AU  - Crocker, P.
PY  - 2023
DO  - 10.23919/cisti58278.2023.10211536
CY  - Aveiro
UR  - https://ieeexplore.ieee.org/document/10211536/authors#authors
AB  - his article presents an analysis of sentiment classification algorithms, based on texts in Portuguese (Pt-Br) extracted from Twitter and Steam platforms, to determine which are the best Analysis Sentiment algorithms to classify user feedback in digital game contexts. On the Twitter platform, the best algorithm was Stacking with Support Vector Machine meta- classifier reaching 81.5% Accuracy. On the Steam platform, the best algorithm was Stacking with Random Forest meta-classifier reaching 82.8% Accuracy. The results show that the performance of each algorithm tends to improve when using Steam data.
ER  -