Open AccessSocial network extraction based on Web: A Review about Supervised Methods
Author(s) -
Mahyuddin K. M. Nasution,
Shahrul Azman Mohd Noah
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1898/1/012046
Subject(s) - computer science , information extraction , process (computing) , social network (sociolinguistics) , data extraction , extraction (chemistry) , data science , information retrieval , data mining , artificial intelligence , social media , world wide web , chemistry , medline , chromatography , political science , law , operating system
The extraction of social networks from specific sources of information is essential. It relates to the disclosure of social structures with prevailing behavior in accordance with that information source. It, of course, requires methods that are generally in a supervised stream. The method changes based on the demands of data modeling, which are generally textual, but do not rule out other types of information, such as databases or different literacy. This paper reviews the methods that have been developed and the types of information sources involved as input to the social network extraction process. This brief review follows the literature related to social network extraction involving supervised methods. Based on different information sources, there are different models in supervised stream.