Deceptive review detection using labeled and unlabeled data
- 19 August 2016
- journal article
- research article
- Published by Springer Science and Business Media LLC in Multimedia Tools and Applications
- Vol. 76 (3), 3187-3211
- https://doi.org/10.1007/s11042-016-3819-y
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Communications and Information Technology, Govt. of India (Information Security Education & Awareness Project (Phase II))
- Department of Science and Technology, Government of India (Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Program – 2016)
This publication has 24 references indexed in Scilit:
- Survey of review spam detection using machine learning techniquesJournal of Big Data, 2015
- Live multimedia brand-related data identification in microblogNeurocomputing, 2015
- Multi-modal microblog classification via multi-task learningMultimedia Tools and Applications, 2014
- Detecting Spam Review through Sentiment AnalysisJournal of Software, 2014
- Logo information recognition in large-scale social media dataMultimedia Systems, 2014
- Positive Unlabeled Learning for Deceptive Reviews DetectionPublished by Association for Computational Linguistics (ACL) ,2014
- Spotting opinion spammers using behavioral footprintsPublished by Association for Computing Machinery (ACM) ,2013
- A trust model for multimedia social networksSocial Network Analysis and Mining, 2012
- Text mining and probabilistic language modeling for online review spam detectionACM Transactions on Management Information Systems, 2011
- Building text classifiers using positive and unlabeled examplesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004