LSTM Based Short Message Service (SMS) Modeling for Spam Classification-Reference-Cited by-同舟云学术

LSTM Based Short Message Service (SMS) Modeling for Spam Classification

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 International Conference on Machine Learning Technologies - ICMLT '18
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Author:

Raj Hans¹,Weihong Yao¹,Banbhrani Santosh Kumar¹,Dino Soomro Pir¹

Affiliation:

1. School of Computer Science and Technology, Dalian University of Technology, P.R. China

Publisher

ACM Press

Reference10 articles.

1. Feresa Mohd Foozy, Cik & Ahmad, Rabiah & Abdollah, Mohd & Chuah, Chai Wen. (2017). A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam. IOP Conference Series: Materials Science and Engineering. 226. 012100. 10.1088/1757-899X/226/1/012100.

2. Li, Panpan & Li, Jun & Sun, Feiqiang & Wang, Peng. (2017). Short Text Emotion Analysis Based on Recurrent Neural Network. 1--5. 10.1145/3078564.3078569.

3. Xu, H., Lu, H., Yang, G., & Zhang, C. (2017, August). Sentiment Analysis of Chinese Version Using SVM & RNN. In Proceedings of the 6th International Conference on Information Engineering (p. 5). ACM.

4. Turian, J., Ratinov, L., & Bengio, Y. (2010, July). Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 384--394). Association for Computational Linguistics.

5. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111--3119).

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