Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification
暂无分享,去创建一个
Bedir Tekinerdogan | Cagatay Catal | Alexander Ligthart | B. Tekinerdogan | C. Catal | Alexander Ligthart
[1] M. Omair Shafiq,et al. Large Scale and Parallel Sentiment Analysis Based on Label Propagation in Twitter Data , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).
[2] Estevam R. Hruschka,et al. Using unsupervised information to improve semi-supervised tweet sentiment classification , 2016, Inf. Sci..
[3] Oliver Kramer,et al. Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines , 2012, ICPRAM.
[4] Mohammad Karim Sohrabi,et al. A survey on classification techniques for opinion mining and sentiment analysis , 2017, Artificial Intelligence Review.
[5] Tom M. Mitchell,et al. Semi-Supervised Text Classification Using EM , 2006, Semi-Supervised Learning.
[6] Taghi M. Khoshgoftaar,et al. Survey of review spam detection using machine learning techniques , 2015, Journal of Big Data.
[7] Steven C. H. Hoi,et al. Two-View Transductive Support Vector Machines , 2010, SDM.
[8] Zhoujun Li,et al. SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion , 2018, 2018 IEEE International Conference on Data Mining (ICDM).
[9] Francesco Marcelloni,et al. A survey on fake news and rumour detection techniques , 2019, Inf. Sci..
[10] Jaber Karimpour,et al. Web Spam Detection by Learning from Small Labeled Samples , 2012 .
[11] Christopher Krügel,et al. Detecting Deceptive Reviews Using Generative Adversarial Networks , 2018, 2018 IEEE Security and Privacy Workshops (SPW).
[12] Dae-Ki Kang,et al. Detecting the spam review using tri-training , 2015, 2015 17th International Conference on Advanced Communication Technology (ICACT).
[13] Niddal Imam,et al. A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift , 2019, Int. J. Comput. Intell. Appl..
[14] Rayid Ghani,et al. Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.
[15] David Yarowsky,et al. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.
[16] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[17] Md. Rabiul Islam,et al. Detection of fake online reviews using semi-supervised and supervised learning , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).
[18] Claire Cardie,et al. Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.
[19] Shrutika S. Sawant,et al. A review on graph-based semi-supervised learning methods for hyperspectral image classification , 2020 .
[20] Shuang Wang,et al. Semi-Supervised Learning Based Fake Review Detection , 2017, 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC).
[21] Eduardo R. Hruschka,et al. A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning , 2016, ACM Comput. Surv..
[22] Jitendra Kumar Rout,et al. Review Spam Detection Using Semi-supervised Technique , 2018 .
[23] Qinghua Zheng,et al. Semi-supervised clue fusion for spammer detection in Sina Weibo , 2018, Inf. Fusion.
[24] William J. Tolone,et al. Identifying malicious social media contents using multi-view Context-Aware active learning , 2019, Future Gener. Comput. Syst..
[25] Xiaoli Li,et al. Learning from Positive and Unlabeled Examples with Different Data Distributions , 2005, ECML.
[26] Yoshua Bengio,et al. Inference for the Generalization Error , 1999, Machine Learning.
[27] Jason R. C. Nurse,et al. A Semi-Supervised Approach to Message Stance Classification , 2020, IEEE Transactions on Knowledge and Data Engineering.
[28] B Eswara Reddy,et al. Semi-supervised learning: a brief review , 2018 .
[29] Faisal Muhammad Shah,et al. Review spam detection using active learning , 2016, 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).
[30] Vili Podgorelec,et al. Text classification method based on self-training and LDA topic models , 2017, Expert Syst. Appl..
[31] Hui Wu,et al. Semi-Supervised Recursive Autoencoders for Social Review Spam Detection , 2016, 2016 12th International Conference on Computational Intelligence and Security (CIS).
[32] Jie Cao,et al. Semi-SGD: Semi-Supervised Learning Based Spammer Group Detection in Product Reviews , 2017, 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD).
[33] Sinan Aral,et al. The spread of true and false news online , 2018, Science.
[34] Kim-Kwang Raymond Choo,et al. Revisiting Semi-Supervised Learning for Online Deceptive Review Detection , 2017, IEEE Access.
[35] Ahmet Onur Durahim,et al. SPR2EP: A Semi-Supervised Spam Review Detection Framework , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).