Features Selection for Entity Resolution in Prostitution on Twitter

International Journal of Advances in Data and Information Systems

View Publication Info
 
 
Field Value
 
Title Features Selection for Entity Resolution in Prostitution on Twitter
 
Creator Permatasari, Reisa
Rakhmawati, Nur Aini
 
Subject entity resolution
online prostitution
regularized logistic regression
twitter
 
Description Entity resolution is the process of determining whether two references to real-world objects refer to the same or different purposes. This study applies entity resolution on Twitter prostitution dataset based on features with the Regularized Logistic Regression training and determination of Active Learning on Dedupe and based on graphs using Neo4j and Node2Vec. This study found that maximum similarity is 1 when the number of features (personal, location and bio specifications) is complete. The minimum similarity is 0.025662627 when the amount of harmful training data. The most influencing similarity feature is the cellphone number with the lowest starting range from 0.997678459 to 0.999993523.  The parameter - length of walk per source has the effect of achieving the best similarity accuracy reaching 71.4% (prediction 14 and yield 10).
 
Publisher Indonesian Scientific Journal
 
Date 2021-03-27
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijadis.org/index.php/IJADIS/article/view/features-selection-for-entity-resolution-in-prostitution-on-twit
10.25008/ijadis.v2i1.1214
 
Source International Journal of Advances in Data and Information Systems; Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems; 53-61
2721-3056
 
Language eng
 
Relation http://ijadis.org/index.php/IJADIS/article/view/features-selection-for-entity-resolution-in-prostitution-on-twit/23
 
Rights Copyright (c) 2021 Reisa Permatasari, Nur Aini Rakhmawati
https://creativecommons.org/licenses/by-sa/4.0
 

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