Comparative analysis of TEXT MINING algorithms for identifying agitation data

1. T. S. Guzella & W. M. Caminhas. (2009). A review of machine learning approches to spam filtering. [Elsever, Expert System with Applications] [in English].
2. C.-H. Lee and H.-C. Yang. (2009). Construction of supervised and unsupervised learning systems for multilingualtext categorization. [ExpertSystems with Applications] [in English].
3. Ng, A. Y. and Jordan, M. I. (2002). On discriminative vs. generative classifiers. [A comparison of logistic regression and naive bayes. In NIPS 14] [in English].
4. A. Markov and M. Last, (2005). A simple, structure- sensitive approach forweb document classification. [Atlantic Web Intelligence Conference –AWIC] [in English].
5. Vinciarelli A. (2004) Noisy Text Categorization,Pattern Recognition. [17th International Conference on (ICPR'04)] [in English].
6. S. Chakrabarti, S. Roy & M. V. Soundalgekar (2003). Fast and accurate text classification via multiple linear discriminant projection. [TheInternational Journal on Very Large Data Bases (VLDB)] [in English].
7. Monitoring project of OKO. [Electronic resource]. Retrieved from http://www.ukroko.org/ [in English]

8. Gavrilenko Olena, Oliynik Yuri, Khanko Hanna (2018) Review and analysis of algorithms TEXT MINING [Project management, systems analysis and logistics] [in Ukrainian].