Spam Text Detection
DOI:
https://doi.org/10.32628/CSEIT2173151Keywords:
Spam Detection, unsolicited commercial email, SMS, NLPAbstract
Spam Detection is the process to classify text which contains irrelevant or unsolicited messages sent over the internet, typically to a large number of users, for the purposes of advertising, phishing, spreading malware, etc. Text summarization is the technique of converting long text to short. The intention is to make a coherent and fluent summary having only the most points outlined within the document. A USA based machine learning expert which had 13 years of experience and currently teaches people his skills, states his technique has proved to be critical in quickly and accurately summarizing voluminous texts, something which might be expensive and time-consuming if avoided machines. Machine learning models are usually trained to know documents and distil the useful information before outputting the specified summarized texts.
References
- I. Androutsopoulos, J. Koutsias, K. Chandrinos, and C. Spyropoulos. An experimental comparison of Naive Bayesian and keyword-based anti-spam filtering with encrypted personal e-mail messages. In 23rd ACM SIGIR Conference, pages 160–167, Athens, Greece, 2000.
- A. McCallum and K. Nigam. A comparison of event models for Naive Bayes text classification. In AAAI Workshop on Learning for Text Categorization, pages 41–48, Madison, Wisconsin, 1998.
- V. Metsis, I. Androutsopoulos, and G. Paliouras. Spam Filtering with Naive Bayes – Which Naive Bayes? In Proceedings of 3rd Conference on E-mail and Anti-Spam (CEAS 2006), Mountain View, CA, USA, 2006
- J. D. M. Rennie, L. Shih, and D. R. Karger. Tackling the Poor Assumptions of Naive Bayes text Classifiers. In 20th International Conference on Machine Learning, Washington DC, 2003.
- M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian approach to filtering junk e-mail. In Learning for Text Categorization – Papers from the AAAI Workshop, pages 550–62, Madison, Wisconsin, 1998
- K.-M. Schneider. A comparison of event models for Naive Bayes anti-spam e-mail filtering. In 10th Conference of the European Chapter of the ACL, pages 307–314, Budapest, Hungary, 2003
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