Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/68018
Título: | Exploring data analytics of data variety |
Autor(es): | Cruz, Tiago Oliveira e Sá, Jorge Pereira, José Luís |
Palavras-chave: | Sentimental and emotional analysis Machine learning Data analysis techniques |
Data: | 2018 |
Editora: | Springer |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | Cruz T., Oliveira e Sá J., Pereira J.L. (2018) Exploring Data Analytics of Data Variety. In: Rocha Á., Adeli H., Reis L., Costanzo S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_87 |
Resumo(s): | The Internet allows organizations managers access to large amounts of data, and this data are presented in different formats, i.e., data variety, namely structured, semi-structured and unstructured. Based on the Internet, this data variety is partly derived from social networks, but not only, machines are also capable of sharing information among themselves, or even machines with people. The objective of this paper is to understand how to retrieve information from data analysis with data variety. An experiment was carried out, based on a dataset with two distinct data types, images and comments on cars. Techniques of data analysis were used, namely Natural Language Processing to identify patterns, and Sentimental and Emotional Analysis. The image recognition technique was used to associate a car model with a category. Next, OLAP cubes and their visualization through dashboards were created. This paper concludes that it is possible to extract a set of relevant information, namely identifying which cars people like more/less, among other information. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/68018 |
ISBN: | 978-3-319-77711-5 |
e-ISBN: | 978-3-319-77712-2 |
DOI: | 10.1007/978-3-319-77712-2_87 |
ISSN: | 2194-5357 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-319-77712-2_87 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Data Analytics_english_VF.pdf | 388 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons