This talk aims to provide an information about where and how one could start using simple text-classification models. Additionally it will be shown how a python classificator can be incorporated into existing system. The presentation will be broken into 3 topics and a conclusion.
First, the presentation provides an overview of how the problem was approached, what information was useful or not and how the technologies tack shown in the second part was decided on.
Second part will concentrate on using Naive Bayesian model for text classification. How the model was trained, what difficulties were met and how they were solved. Additionally the talk will give a brief overview of other possible model choices (random forest, SVM).
The third part will show how the model was deployed and used in the production. One architecture solution will be shown in details (REST calls between Java Client and Flask Server), while other possibilities will be mentioned briefly.
As the conclusion the possible improvements for the model in use will be suggested as well as short example of supervised learning algorithm (CNN) and unsupervised classification algorithm (LDA) for the same purpose. Along with the examples the proc and cons will be named.
Technologies mentioned and used: Flask, Green Unicorn vs uWSGI, NLTK, Sci-Kit, Python 3, Java 8, Jersey, Docker, Kubernetes