Abstract
Classification of text is very important and interesting topic for research due to number of unstructured data and especially in the form of news. The problem arises when classifying news that is written in language other than english. Due to poor support of research done in this field and general language complexity, it is very hard to identify correctly, tone and sentiment of a text. This problems can be avoided by applying various techniques at many stages of this model. Choosing proper technique can be very tricky and would affect efficiency of a model and can save time as well as working time. The aim of this paper is to compare traditional machine learning techniques with Google services by using Translate API and see how well they perform while working with Russian texts. On traditional machine learning techniques after applying pre-processing methods which included stemming, parsing and indexing and by using various different methods of embedding turns our unstructured data into a structured data.